{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "cellView": "both",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 309
    },
    "colab_type": "code",
    "id": "AKP2Vok31tsE",
    "outputId": "f268f545-a908-4e75-f957-dad08b72c277"
   },
   "outputs": [],
   "source": [
    "#@title Install arviz\n",
    "# !pip3 install arviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "a0disJ2Uy-Bx"
   },
   "outputs": [],
   "source": [
    "import arviz as az\n",
    "import pystan\n",
    "import os\n",
    "# os.environ['STAN_NUM_THREADS'] = \"4\"\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import networkx as nx\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YT3IVNpzrBoM"
   },
   "source": [
    "## Select model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-UJBadMvq8aM"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "        About: \n",
      "\n",
      "        SICRDq model \n",
      "\n",
      "        Some unknown I and known C, both go to recovered and death \n",
      "\n",
      "        I and C have same leak to r and d but different to total infected Z\n",
      "        \n",
      "S: susceptible\n",
      "I: infected\n",
      "C: identified cases\n",
      "R: recovered\n",
      "D: dead\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/networkx/drawing/nx_pylab.py:579: MatplotlibDeprecationWarning: \n",
      "The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.\n",
      "  if not cb.iterable(width):\n",
      "/usr/local/lib/python3.7/site-packages/networkx/drawing/nx_pylab.py:676: MatplotlibDeprecationWarning: \n",
      "The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.\n",
      "  if cb.iterable(node_size):  # many node sizes\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import MBS_epidemic_concentration_models as models\n",
    "model = models.model_sicrq()\n",
    "model.plotnetwork()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_af361cf5a57a13f67f8ebb8d9717b9ae NOW.\n"
     ]
    }
   ],
   "source": [
    "stanrunmodel = pystan.StanModel(model_code=model.stan)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qn5Cei_mLN0P"
   },
   "source": [
    "# Load data from JHU\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ofuI7UGsLNd7"
   },
   "outputs": [],
   "source": [
    "url_confirmed = \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\"\n",
    "url_deaths = \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv\"\n",
    "url_recovered = \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv\"\n",
    "\n",
    "dfc = pd.read_csv(url_confirmed)\n",
    "dfd = pd.read_csv(url_deaths)\n",
    "dfr = pd.read_csv(url_recovered)\n",
    "\n",
    "# print(dfc)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JQDsWrTVteCu"
   },
   "source": [
    "## Make JHU ROI DF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Enter country "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Austria,Belgium,Denmark,France,Germany,Italy,Norway,Spain,Sweden,Switzerland,United Kingdom\n",
    "roi = \"Italy\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XDoHvSi-NQRQ"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t0 assumed to be: day 29\n",
      "2/20/20\n",
      "3/9/20\n",
      "47\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x136768890>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfc2 = dfc.loc[(dfc['Country/Region']==roi)&(pd.isnull(dfc['Province/State']))]\n",
    "dfd2 = dfd.loc[(dfd['Country/Region']==roi)&(pd.isnull(dfd['Province/State']))]\n",
    "dfr2 = dfr.loc[(dfr['Country/Region']==roi)&(pd.isnull(dfr['Province/State']))]\n",
    "\n",
    "\n",
    "DF = df = pd.DataFrame(columns=['date','cases','recovered','deaths'])\n",
    "\n",
    "dates_all = dfc.columns[4:].values[:-1]\n",
    "\n",
    "dates = dates_all[:]\n",
    "\n",
    "for i in range(len(dates)):\n",
    "  DF.loc[i] = pd.Series({'date':dates[i],\n",
    "                         'cases':dfc2[dates[i]].values[0] - (dfr2[dates[i]].values[0] + dfd2[dates[i]].values[0]),\n",
    "                         'recovered':dfr2[dates[i]].values[0],\n",
    "                         'deaths':dfd2[dates[i]].values[0]})\n",
    "\n",
    "\n",
    "pop = {}\n",
    "pop['Italy'] = 60500000\n",
    "pop['United Kingdom'] = 64400000\n",
    "pop['France'] = 66990000\n",
    "\n",
    "mitigate = {}\n",
    "mitigate['Italy'] = '3/9/20' #approximate date\n",
    "\n",
    "t0 = np.where(DF[\"cases\"].values>5)[0][0] - 1# estimated day of first exposure? Need to make this a parameter\n",
    "model.stan_data['t0'] = t0-1\n",
    "print(\"t0 assumed to be: day \"+str(t0))\n",
    "plt.plot(DF[\"cases\"],'bo', label=\"cases\")\n",
    "plt.plot(DF[\"recovered\"],'go',label=\"recovered\")\n",
    "plt.plot(DF[\"deaths\"],'ks',label=\"deaths\")\n",
    "\n",
    "plt.axvline(model.stan_data['t0'],color='k', linestyle=\"dashed\", label='t0')\n",
    "\n",
    "ind = np.where(mitigate[roi]==dates)[0][0]\n",
    "plt.axvline(ind,color='b', linestyle=\"dashed\", label='mitigate')\n",
    "print(dates[t0])\n",
    "print(dates[ind])\n",
    "print(ind)\n",
    "\n",
    "model.stan_data['tm'] = ind\n",
    "\n",
    "plt.legend()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gLSieVFFtjOb"
   },
   "source": [
    "## Format JHU ROI data for Stan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 299
    },
    "colab_type": "code",
    "id": "iG4K7TG6NTJI",
    "outputId": "12052154-8907-4d79-d85a-7823584d3aca",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#truncate time series from t0 on (initial is t0-1)\n",
    "model.stan_data['ts'] = np.arange(t0,len(dates))  \n",
    "model.stan_data['y'] = (DF[['cases','recovered','deaths']].to_numpy()).astype(int)[t0:,:]\n",
    "model.stan_data['n_obs'] = len(dates) - t0\n",
    "\n",
    "model.stan_data['ts_predict'] = np.arange(t0,len(dates)+365)\n",
    "model.stan_data['n_obs_predict'] = len(dates) - t0 + 365"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Enter population manually"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.stan_data['n_pop'] = pop[roi] \n",
    "model.stan_data['n_scale'] = 10000000\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Print data for Stan "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_theta': 6, 'n_difeq': 5, 'n_ostates': 3, 't0': 28, 'tm': 47, 'ts': array([29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45,\n",
      "       46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62,\n",
      "       63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76]), 'y': array([[    3,     0,     0],\n",
      "       [   19,     0,     1],\n",
      "       [   59,     1,     2],\n",
      "       [  150,     2,     3],\n",
      "       [  221,     1,     7],\n",
      "       [  311,     1,    10],\n",
      "       [  438,     3,    12],\n",
      "       [  593,    45,    17],\n",
      "       [  821,    46,    21],\n",
      "       [ 1053,    46,    29],\n",
      "       [ 1577,    83,    34],\n",
      "       [ 1835,   149,    52],\n",
      "       [ 2263,   160,    79],\n",
      "       [ 2706,   276,   107],\n",
      "       [ 3296,   414,   148],\n",
      "       [ 3916,   523,   197],\n",
      "       [ 5061,   589,   233],\n",
      "       [ 6387,   622,   366],\n",
      "       [ 7985,   724,   463],\n",
      "       [ 8794,   724,   631],\n",
      "       [10590,  1045,   827],\n",
      "       [10590,  1045,   827],\n",
      "       [14955,  1439,  1266],\n",
      "       [17750,  1966,  1441],\n",
      "       [20603,  2335,  1809],\n",
      "       [23073,  2749,  2158],\n",
      "       [26062,  2941,  2503],\n",
      "       [28710,  4025,  2978],\n",
      "       [33190,  4440,  3405],\n",
      "       [38549,  4440,  4032],\n",
      "       [42681,  6072,  4825],\n",
      "       [46638,  7024,  5476],\n",
      "       [50826,  7024,  6077],\n",
      "       [54030,  8326,  6820],\n",
      "       [57521,  9362,  7503],\n",
      "       [62013, 10361,  8215],\n",
      "       [66414, 10950,  9134],\n",
      "       [70065, 12384, 10023],\n",
      "       [73880, 13030, 10779],\n",
      "       [75528, 14620, 11591],\n",
      "       [77635, 15729, 12428],\n",
      "       [80572, 16847, 13155],\n",
      "       [83049, 18278, 13915],\n",
      "       [85388, 19758, 14681],\n",
      "       [88274, 20996, 15362],\n",
      "       [91246, 21815, 15887],\n",
      "       [93187, 22837, 16523],\n",
      "       [94067, 24392, 17127]]), 'n_obs': 48, 'ts_predict': array([ 29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,\n",
      "        42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,\n",
      "        55,  56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,\n",
      "        68,  69,  70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,\n",
      "        81,  82,  83,  84,  85,  86,  87,  88,  89,  90,  91,  92,  93,\n",
      "        94,  95,  96,  97,  98,  99, 100, 101, 102, 103, 104, 105, 106,\n",
      "       107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119,\n",
      "       120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132,\n",
      "       133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145,\n",
      "       146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158,\n",
      "       159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171,\n",
      "       172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184,\n",
      "       185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197,\n",
      "       198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210,\n",
      "       211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223,\n",
      "       224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236,\n",
      "       237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249,\n",
      "       250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262,\n",
      "       263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275,\n",
      "       276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288,\n",
      "       289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301,\n",
      "       302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314,\n",
      "       315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327,\n",
      "       328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340,\n",
      "       341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353,\n",
      "       354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366,\n",
      "       367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379,\n",
      "       380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392,\n",
      "       393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405,\n",
      "       406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418,\n",
      "       419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431,\n",
      "       432, 433, 434, 435, 436, 437, 438, 439, 440, 441]), 'n_obs_predict': 413, 'n_pop': 60500000, 'n_scale': 10000000}\n"
     ]
    }
   ],
   "source": [
    "print(model.stan_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load England School 1978 Influenza data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #England 1978 influenza\n",
    "# cases = [0,8,26,76,225,298,258,233,189,128,150,85,14,4]\n",
    "# recovered = [0,0,0,0,9,17,105,162,176,166,150,85,47,20]\n",
    "# plt.plot(cases,'bo', label=\"cases\")\n",
    "# plt.plot(recovered,'go',label=\"recovered\")\n",
    "# pop = 763\n",
    "# model.stan_data['t0'] = 0\n",
    "# #truncate time series from t0 on (initial is t0-1)\n",
    "# model.stan_data['n_pop'] = pop \n",
    "# model.stan_data['ts'] = np.arange(1,len(cases)+1)  \n",
    "# Y = np.hstack([np.c_[cases],np.c_[recovered],np.zeros((len(cases),1))]).astype(int)\n",
    "# model.stan_data['y'] = Y\n",
    "# model.stan_data['n_obs'] = len(cases)\n",
    "\n",
    "# plt.plot(cases,'bo', label=\"cases\")\n",
    "# plt.plot(recovered,'go',label=\"recovered\")\n",
    "\n",
    "# plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GDWmF_KQ2Hg3"
   },
   "source": [
    "# Run Stan "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oImIcxX9Lull"
   },
   "outputs": [],
   "source": [
    "# Feed in some feasible initial values to start from\n",
    "\n",
    "# init_par = [{'theta':[0.25,0.01,0.01,0.05,.02],'S0':0.5}] \n",
    "\n",
    "\n",
    "if model.name in [\"sir1\",\"sir2\"]:\n",
    "    def init_fun():\n",
    "        x = {'theta':\n",
    "             [0.5*np.random.uniform()]+\n",
    "             [0.1*np.random.uniform()]\n",
    "            }\n",
    "        return x\n",
    "\n",
    "if model.name in [\"sicu\"]:\n",
    "    def init_fun():\n",
    "        x = {'theta':\n",
    "             [0.1*np.random.uniform()]+\n",
    "             [0.1*np.random.uniform()]+\n",
    "             [0.5*np.random.uniform()]+\n",
    "             [0.1*np.random.uniform()]\n",
    "            }\n",
    "        return x\n",
    "    \n",
    "if model.name in [\"sicuq\",\"sicuf\"]:\n",
    "    def init_fun():\n",
    "        x = {'theta':\n",
    "             [0.1*np.random.uniform()]+\n",
    "             [0.1*np.random.uniform()]+\n",
    "             [0.5*np.random.uniform()]+\n",
    "             [0.1*np.random.uniform()]+\n",
    "             [0.1*np.random.uniform()]\n",
    "            }\n",
    "        return x\n",
    "    \n",
    "if model.name in [\"sicrqm\"]:\n",
    "    def init_fun():\n",
    "        x = {'theta':\n",
    "             [0.1*np.random.uniform()]+\n",
    "             [0.1*np.random.uniform()]+\n",
    "             [0.1*np.random.uniform()]+\n",
    "             [0.1*np.random.uniform()]+\n",
    "             [0.5*np.random.uniform()]+\n",
    "             [0.5*np.random.uniform()]+\n",
    "             [7*np.random.uniform()]+\n",
    "             [0.1*np.random.uniform()]}\n",
    "        return x\n",
    "\n",
    "    \n",
    "\n",
    "if model.name in [\"sicrq\"]:\n",
    "    def init_fun():\n",
    "        x = {'theta':\n",
    "             [np.random.lognormal(np.log(0.1),1)]+\n",
    "             [np.random.lognormal(np.log(0.1),1)]+\n",
    "             [np.random.lognormal(np.log(0.1),1)]+\n",
    "             [np.random.lognormal(np.log(0.1),1)]+\n",
    "             [np.random.lognormal(np.log(0.25),1)]+\n",
    "             [np.random.lognormal(np.log(0.1),1)]}\n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fit Stan "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 429
    },
    "colab_type": "code",
    "id": "3rtwA8puqVrv",
    "outputId": "061a2610-4f20-4cb2-c8fd-10d97d845fbe"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n"
     ]
    }
   ],
   "source": [
    "n_chains=8\n",
    "n_warmups=1000\n",
    "n_iter=10000\n",
    "n_thin=50\n",
    "\n",
    "control = {'adapt_delta':0.95}\n",
    "fit = stanrunmodel.sampling(data = model.stan_data,init = init_fun,control=control, chains = n_chains, warmup = n_warmups, iter = n_iter, thin=n_thin, seed=13219)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2jXKxrCf3Vj2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inference for Stan model: anon_model_af361cf5a57a13f67f8ebb8d9717b9ae.\n",
      "8 chains, each with iter=10000; warmup=1000; thin=50; \n",
      "post-warmup draws per chain=180, total post-warmup draws=1440.\n",
      "\n",
      "                   mean se_mean     sd   2.5%    25%    50%    75%  97.5%  n_eff   Rhat\n",
      "theta[1]         9.0e-4  5.8e-8 2.3e-6 9.0e-4 9.0e-4 9.0e-4 9.0e-4 9.1e-4   1559    1.0\n",
      "theta[2]           0.02  9.8e-7 3.7e-5   0.02   0.02   0.02   0.02   0.02   1407    1.0\n",
      "theta[3]           0.01  8.4e-7 3.1e-5   0.01   0.01   0.01   0.01   0.01   1372    1.0\n",
      "theta[4]           0.13  4.5e-3   0.15   0.01   0.05   0.09   0.17   0.55   1070    1.0\n",
      "theta[5]           0.25  1.7e-5 5.9e-4   0.24   0.25   0.25   0.25   0.25   1234    1.0\n",
      "theta[6]          4.0e4    9.32 346.34  3.9e4  3.9e4  4.0e4  4.0e4  4.0e4   1382    1.0\n",
      "u[1,1]           4.3e-6  1.0e-9 3.8e-8 4.2e-6 4.3e-6 4.3e-6 4.3e-6 4.4e-6   1356    1.0\n",
      "u[2,1]           9.1e-6  2.2e-9 8.0e-8 9.0e-6 9.1e-6 9.1e-6 9.2e-6 9.3e-6   1358    1.0\n",
      "u[3,1]           1.5e-5  3.5e-9 1.3e-7 1.5e-5 1.5e-5 1.5e-5 1.5e-5 1.5e-5   1361    1.0\n",
      "u[4,1]           2.2e-5  5.0e-9 1.8e-7 2.2e-5 2.2e-5 2.2e-5 2.2e-5 2.3e-5   1363    1.0\n",
      "u[5,1]           3.1e-5  6.6e-9 2.5e-7 3.0e-5 3.1e-5 3.1e-5 3.1e-5 3.1e-5   1367    1.0\n",
      "u[6,1]           4.2e-5  8.5e-9 3.2e-7 4.1e-5 4.1e-5 4.2e-5 4.2e-5 4.2e-5   1371    1.0\n",
      "u[7,1]           5.5e-5  1.1e-8 3.9e-7 5.4e-5 5.4e-5 5.5e-5 5.5e-5 5.6e-5   1377    1.0\n",
      "u[8,1]           7.1e-5  1.3e-8 4.8e-7 7.0e-5 7.1e-5 7.1e-5 7.1e-5 7.2e-5   1383    1.0\n",
      "u[9,1]           9.1e-5  1.5e-8 5.8e-7 9.0e-5 9.0e-5 9.1e-5 9.1e-5 9.2e-5   1391    1.0\n",
      "u[10,1]          1.1e-4  1.8e-8 6.8e-7 1.1e-4 1.1e-4 1.1e-4 1.2e-4 1.2e-4   1401    1.0\n",
      "u[11,1]          1.4e-4  2.1e-8 7.9e-7 1.4e-4 1.4e-4 1.4e-4 1.5e-4 1.5e-4   1413    1.0\n",
      "u[12,1]          1.8e-4  2.4e-8 9.1e-7 1.8e-4 1.8e-4 1.8e-4 1.8e-4 1.8e-4   1427    1.0\n",
      "u[13,1]          2.2e-4  2.7e-8 1.0e-6 2.2e-4 2.2e-4 2.2e-4 2.3e-4 2.3e-4   1445    1.0\n",
      "u[14,1]          2.8e-4  3.1e-8 1.2e-6 2.8e-4 2.8e-4 2.8e-4 2.8e-4 2.8e-4   1467    1.0\n",
      "u[15,1]          3.4e-4  3.4e-8 1.3e-6 3.4e-4 3.4e-4 3.4e-4 3.4e-4 3.5e-4   1493    1.0\n",
      "u[16,1]          4.2e-4  3.7e-8 1.4e-6 4.2e-4 4.2e-4 4.2e-4 4.2e-4 4.3e-4   1524    1.0\n",
      "u[17,1]          5.2e-4  4.0e-8 1.6e-6 5.1e-4 5.2e-4 5.2e-4 5.2e-4 5.2e-4   1559    1.0\n",
      "u[18,1]          6.3e-4  4.3e-8 1.7e-6 6.2e-4 6.3e-4 6.3e-4 6.3e-4 6.3e-4   1594    1.0\n",
      "u[19,1]          7.6e-4  4.6e-8 1.9e-6 7.6e-4 7.6e-4 7.6e-4 7.6e-4 7.6e-4   1624    1.0\n",
      "u[20,1]          9.2e-4  5.1e-8 2.0e-6 9.1e-4 9.1e-4 9.2e-4 9.2e-4 9.2e-4   1636    1.0\n",
      "u[21,1]          1.1e-3  5.6e-8 2.3e-6 1.1e-3 1.1e-3 1.1e-3 1.1e-3 1.1e-3   1624    1.0\n",
      "u[22,1]          1.3e-3  6.4e-8 2.5e-6 1.3e-3 1.3e-3 1.3e-3 1.3e-3 1.3e-3   1571    1.0\n",
      "u[23,1]          1.5e-3  7.8e-8 2.8e-6 1.5e-3 1.5e-3 1.5e-3 1.5e-3 1.5e-3   1334    1.0\n",
      "u[24,1]          1.8e-3  9.2e-8 3.2e-6 1.8e-3 1.8e-3 1.8e-3 1.8e-3 1.8e-3   1202   1.01\n",
      "u[25,1]          2.1e-3  1.1e-7 3.6e-6 2.1e-3 2.1e-3 2.1e-3 2.1e-3 2.1e-3   1134   1.01\n",
      "u[26,1]          2.4e-3  1.2e-7 4.0e-6 2.4e-3 2.4e-3 2.4e-3 2.4e-3 2.4e-3   1090   1.01\n",
      "u[27,1]          2.7e-3  1.4e-7 4.4e-6 2.7e-3 2.7e-3 2.7e-3 2.7e-3 2.7e-3   1047   1.01\n",
      "u[28,1]          3.1e-3  1.5e-7 4.8e-6 3.1e-3 3.1e-3 3.1e-3 3.1e-3 3.1e-3   1056   1.01\n",
      "u[29,1]          3.5e-3  1.6e-7 5.1e-6 3.5e-3 3.5e-3 3.5e-3 3.5e-3 3.5e-3   1056   1.01\n",
      "u[30,1]          3.8e-3  1.6e-7 5.3e-6 3.8e-3 3.8e-3 3.8e-3 3.8e-3 3.9e-3   1049   1.01\n",
      "u[31,1]          4.2e-3  1.7e-7 5.4e-6 4.2e-3 4.2e-3 4.2e-3 4.2e-3 4.2e-3   1027   1.01\n",
      "u[32,1]          4.6e-3  1.7e-7 5.5e-6 4.6e-3 4.6e-3 4.6e-3 4.6e-3 4.6e-3   1008   1.01\n",
      "u[33,1]          5.0e-3  1.7e-7 5.5e-6 5.0e-3 5.0e-3 5.0e-3 5.0e-3 5.0e-3    997   1.01\n",
      "u[34,1]          5.4e-3  1.7e-7 5.4e-6 5.4e-3 5.4e-3 5.4e-3 5.4e-3 5.4e-3    998   1.01\n",
      "u[35,1]          5.8e-3  1.7e-7 5.4e-6 5.8e-3 5.8e-3 5.8e-3 5.8e-3 5.8e-3   1016   1.01\n",
      "u[36,1]          6.2e-3  1.6e-7 5.4e-6 6.1e-3 6.2e-3 6.2e-3 6.2e-3 6.2e-3   1052   1.01\n",
      "u[37,1]          6.5e-3  1.6e-7 5.4e-6 6.5e-3 6.5e-3 6.5e-3 6.5e-3 6.5e-3   1112   1.01\n",
      "u[38,1]          6.9e-3  1.6e-7 5.6e-6 6.8e-3 6.9e-3 6.9e-3 6.9e-3 6.9e-3   1199   1.01\n",
      "u[39,1]          7.2e-3  1.6e-7 5.9e-6 7.2e-3 7.2e-3 7.2e-3 7.2e-3 7.2e-3   1305    1.0\n",
      "u[40,1]          7.5e-3  1.7e-7 6.3e-6 7.5e-3 7.5e-3 7.5e-3 7.5e-3 7.5e-3   1417    1.0\n",
      "u[41,1]          7.8e-3  1.8e-7 6.9e-6 7.8e-3 7.8e-3 7.8e-3 7.8e-3 7.8e-3   1518    1.0\n",
      "u[42,1]          8.1e-3  1.9e-7 7.5e-6 8.0e-3 8.1e-3 8.1e-3 8.1e-3 8.1e-3   1609    1.0\n",
      "u[43,1]          8.3e-3  2.0e-7 8.2e-6 8.3e-3 8.3e-3 8.3e-3 8.3e-3 8.3e-3   1646    1.0\n",
      "u[44,1]          8.6e-3  2.2e-7 8.9e-6 8.5e-3 8.5e-3 8.6e-3 8.6e-3 8.6e-3   1661    1.0\n",
      "u[45,1]          8.8e-3  2.4e-7 9.7e-6 8.8e-3 8.8e-3 8.8e-3 8.8e-3 8.8e-3   1667    1.0\n",
      "u[46,1]          9.0e-3  2.6e-7 1.0e-5 9.0e-3 9.0e-3 9.0e-3 9.0e-3 9.0e-3   1669    1.0\n",
      "u[47,1]          9.2e-3  2.8e-7 1.1e-5 9.1e-3 9.2e-3 9.2e-3 9.2e-3 9.2e-3   1667    1.0\n",
      "u[48,1]          9.3e-3  2.9e-7 1.2e-5 9.3e-3 9.3e-3 9.3e-3 9.3e-3 9.4e-3   1662    1.0\n",
      "u[1,2]           3.9e-8 9.0e-123.3e-10 3.8e-8 3.9e-8 3.9e-8 3.9e-8 3.9e-8   1366    1.0\n",
      "u[2,2]           1.5e-7 3.7e-11 1.4e-9 1.5e-7 1.5e-7 1.5e-7 1.5e-7 1.6e-7   1366    1.0\n",
      "u[3,2]           3.6e-7 8.8e-11 3.3e-9 3.6e-7 3.6e-7 3.6e-7 3.6e-7 3.7e-7   1367    1.0\n",
      "u[4,2]           6.8e-7 1.6e-10 6.1e-9 6.7e-7 6.8e-7 6.8e-7 6.9e-7 6.9e-7   1369    1.0\n",
      "u[5,2]           1.1e-6 2.7e-10 9.9e-9 1.1e-6 1.1e-6 1.1e-6 1.1e-6 1.2e-6   1371    1.0\n",
      "u[6,2]           1.8e-6 4.0e-10 1.5e-8 1.7e-6 1.8e-6 1.8e-6 1.8e-6 1.8e-6   1373    1.0\n",
      "u[7,2]           2.6e-6 5.7e-10 2.1e-8 2.6e-6 2.6e-6 2.6e-6 2.6e-6 2.6e-6   1376    1.0\n",
      "u[8,2]           3.7e-6 7.9e-10 2.9e-8 3.6e-6 3.7e-6 3.7e-6 3.7e-6 3.7e-6   1379    1.0\n",
      "u[9,2]           5.1e-6  1.0e-9 3.9e-8 5.0e-6 5.1e-6 5.1e-6 5.1e-6 5.2e-6   1382    1.0\n",
      "u[10,2]          6.9e-6  1.3e-9 5.0e-8 6.8e-6 6.8e-6 6.9e-6 6.9e-6 6.9e-6   1387    1.0\n",
      "u[11,2]          9.1e-6  1.7e-9 6.3e-8 9.0e-6 9.0e-6 9.1e-6 9.1e-6 9.2e-6   1392    1.0\n",
      "u[12,2]          1.2e-5  2.1e-9 7.9e-8 1.2e-5 1.2e-5 1.2e-5 1.2e-5 1.2e-5   1398    1.0\n",
      "u[13,2]          1.5e-5  2.6e-9 9.7e-8 1.5e-5 1.5e-5 1.5e-5 1.5e-5 1.6e-5   1404    1.0\n",
      "u[14,2]          2.0e-5  3.1e-9 1.2e-7 2.0e-5 2.0e-5 2.0e-5 2.0e-5 2.0e-5   1412    1.0\n",
      "u[15,2]          2.5e-5  3.7e-9 1.4e-7 2.5e-5 2.5e-5 2.5e-5 2.5e-5 2.5e-5   1422    1.0\n",
      "u[16,2]          3.2e-5  4.3e-9 1.6e-7 3.1e-5 3.2e-5 3.2e-5 3.2e-5 3.2e-5   1432    1.0\n",
      "u[17,2]          4.0e-5  5.0e-9 1.9e-7 3.9e-5 4.0e-5 4.0e-5 4.0e-5 4.0e-5   1444    1.0\n",
      "u[18,2]          5.0e-5  5.8e-9 2.2e-7 4.9e-5 5.0e-5 5.0e-5 5.0e-5 5.0e-5   1458    1.0\n",
      "u[19,2]          6.2e-5  6.6e-9 2.5e-7 6.1e-5 6.2e-5 6.2e-5 6.2e-5 6.2e-5   1473    1.0\n",
      "u[20,2]          7.6e-5  7.4e-9 2.9e-7 7.6e-5 7.6e-5 7.6e-5 7.6e-5 7.7e-5   1489    1.0\n",
      "u[21,2]          9.4e-5  8.3e-9 3.2e-7 9.3e-5 9.3e-5 9.4e-5 9.4e-5 9.4e-5   1503    1.0\n",
      "u[22,2]          1.1e-4  9.3e-9 3.6e-7 1.1e-4 1.1e-4 1.1e-4 1.1e-4 1.2e-4   1508    1.0\n",
      "u[23,2]          1.4e-4  1.0e-8 4.0e-7 1.4e-4 1.4e-4 1.4e-4 1.4e-4 1.4e-4   1510    1.0\n",
      "u[24,2]          1.7e-4  1.2e-8 4.5e-7 1.7e-4 1.7e-4 1.7e-4 1.7e-4 1.7e-4   1508    1.0\n",
      "u[25,2]          2.0e-4  1.3e-8 5.0e-7 2.0e-4 2.0e-4 2.0e-4 2.0e-4 2.0e-4   1500    1.0\n",
      "u[26,2]          2.4e-4  1.5e-8 5.6e-7 2.4e-4 2.4e-4 2.4e-4 2.4e-4 2.4e-4   1488    1.0\n",
      "u[27,2]          2.8e-4  1.7e-8 6.4e-7 2.8e-4 2.8e-4 2.8e-4 2.9e-4 2.9e-4   1471    1.0\n",
      "u[28,2]          3.4e-4  1.9e-8 7.2e-7 3.3e-4 3.3e-4 3.4e-4 3.4e-4 3.4e-4   1452    1.0\n",
      "u[29,2]          3.9e-4  2.2e-8 8.1e-7 3.9e-4 3.9e-4 3.9e-4 3.9e-4 3.9e-4   1432    1.0\n",
      "u[30,2]          4.6e-4  2.5e-8 9.2e-7 4.5e-4 4.5e-4 4.6e-4 4.6e-4 4.6e-4   1412    1.0\n",
      "u[31,2]          5.3e-4  2.8e-8 1.0e-6 5.2e-4 5.2e-4 5.3e-4 5.3e-4 5.3e-4   1395    1.0\n",
      "u[32,2]          6.0e-4  3.2e-8 1.2e-6 6.0e-4 6.0e-4 6.0e-4 6.0e-4 6.0e-4   1380    1.0\n",
      "u[33,2]          6.9e-4  3.6e-8 1.3e-6 6.8e-4 6.9e-4 6.9e-4 6.9e-4 6.9e-4   1368    1.0\n",
      "u[34,2]          7.8e-4  4.1e-8 1.5e-6 7.7e-4 7.8e-4 7.8e-4 7.8e-4 7.8e-4   1358    1.0\n",
      "u[35,2]          8.7e-4  4.6e-8 1.7e-6 8.7e-4 8.7e-4 8.7e-4 8.7e-4 8.8e-4   1351    1.0\n",
      "u[36,2]          9.8e-4  5.1e-8 1.9e-6 9.7e-4 9.8e-4 9.8e-4 9.8e-4 9.8e-4   1347    1.0\n",
      "u[37,2]          1.1e-3  5.6e-8 2.1e-6 1.1e-3 1.1e-3 1.1e-3 1.1e-3 1.1e-3   1345    1.0\n",
      "u[38,2]          1.2e-3  6.2e-8 2.3e-6 1.2e-3 1.2e-3 1.2e-3 1.2e-3 1.2e-3   1344    1.0\n",
      "u[39,2]          1.3e-3  6.9e-8 2.5e-6 1.3e-3 1.3e-3 1.3e-3 1.3e-3 1.3e-3   1345    1.0\n",
      "u[40,2]          1.5e-3  7.5e-8 2.8e-6 1.4e-3 1.5e-3 1.5e-3 1.5e-3 1.5e-3   1347    1.0\n",
      "u[41,2]          1.6e-3  8.2e-8 3.0e-6 1.6e-3 1.6e-3 1.6e-3 1.6e-3 1.6e-3   1351    1.0\n",
      "u[42,2]          1.7e-3  8.9e-8 3.3e-6 1.7e-3 1.7e-3 1.7e-3 1.7e-3 1.7e-3   1355    1.0\n",
      "u[43,2]          1.9e-3  9.6e-8 3.6e-6 1.9e-3 1.9e-3 1.9e-3 1.9e-3 1.9e-3   1360    1.0\n",
      "u[44,2]          2.0e-3  1.0e-7 3.8e-6 2.0e-3 2.0e-3 2.0e-3 2.0e-3 2.0e-3   1365    1.0\n",
      "u[45,2]          2.2e-3  1.1e-7 4.2e-6 2.2e-3 2.2e-3 2.2e-3 2.2e-3 2.2e-3   1370    1.0\n",
      "u[46,2]          2.3e-3  1.2e-7 4.5e-6 2.3e-3 2.3e-3 2.3e-3 2.3e-3 2.3e-3   1376    1.0\n",
      "u[47,2]          2.5e-3  1.3e-7 4.8e-6 2.5e-3 2.5e-3 2.5e-3 2.5e-3 2.5e-3   1382    1.0\n",
      "u[48,2]          2.6e-3  1.4e-7 5.1e-6 2.6e-3 2.6e-3 2.6e-3 2.6e-3 2.6e-3   1388    1.0\n",
      "u[1,3]           3.0e-8 7.1e-122.6e-10 2.9e-8 3.0e-8 3.0e-8 3.0e-8 3.0e-8   1304    1.0\n",
      "u[2,3]           1.2e-7 2.9e-11 1.1e-9 1.2e-7 1.2e-7 1.2e-7 1.2e-7 1.2e-7   1312    1.0\n",
      "u[3,3]           2.8e-7 6.9e-11 2.5e-9 2.7e-7 2.8e-7 2.8e-7 2.8e-7 2.8e-7   1312    1.0\n",
      "u[4,3]           5.2e-7 1.3e-10 4.7e-9 5.1e-7 5.2e-7 5.2e-7 5.3e-7 5.3e-7   1312    1.0\n",
      "u[5,3]           8.8e-7 2.1e-10 7.6e-9 8.6e-7 8.7e-7 8.8e-7 8.8e-7 8.9e-7   1312    1.0\n",
      "u[6,3]           1.4e-6 3.2e-10 1.2e-8 1.3e-6 1.3e-6 1.4e-6 1.4e-6 1.4e-6   1312    1.0\n",
      "u[7,3]           2.0e-6 4.5e-10 1.6e-8 2.0e-6 2.0e-6 2.0e-6 2.0e-6 2.0e-6   1312    1.0\n",
      "u[8,3]           2.8e-6 6.2e-10 2.3e-8 2.8e-6 2.8e-6 2.8e-6 2.8e-6 2.9e-6   1312    1.0\n",
      "u[9,3]           3.9e-6 8.3e-10 3.0e-8 3.8e-6 3.9e-6 3.9e-6 3.9e-6 4.0e-6   1312    1.0\n",
      "u[10,3]          5.3e-6  1.1e-9 3.9e-8 5.2e-6 5.2e-6 5.3e-6 5.3e-6 5.3e-6   1312    1.0\n",
      "u[11,3]          7.0e-6  1.4e-9 4.9e-8 6.9e-6 6.9e-6 7.0e-6 7.0e-6 7.1e-6   1312    1.0\n",
      "u[12,3]          9.1e-6  1.7e-9 6.1e-8 9.0e-6 9.1e-6 9.1e-6 9.2e-6 9.3e-6   1312    1.0\n",
      "u[13,3]          1.2e-5  2.1e-9 7.5e-8 1.2e-5 1.2e-5 1.2e-5 1.2e-5 1.2e-5   1312    1.0\n",
      "u[14,3]          1.5e-5  2.5e-9 9.1e-8 1.5e-5 1.5e-5 1.5e-5 1.5e-5 1.5e-5   1313    1.0\n",
      "u[15,3]          1.9e-5  3.0e-9 1.1e-7 1.9e-5 1.9e-5 1.9e-5 1.9e-5 2.0e-5   1313    1.0\n",
      "u[16,3]          2.4e-5  3.5e-9 1.3e-7 2.4e-5 2.4e-5 2.4e-5 2.4e-5 2.5e-5   1314    1.0\n",
      "u[17,3]          3.1e-5  4.1e-9 1.5e-7 3.0e-5 3.1e-5 3.1e-5 3.1e-5 3.1e-5   1314    1.0\n",
      "u[18,3]          3.8e-5  4.8e-9 1.7e-7 3.8e-5 3.8e-5 3.8e-5 3.8e-5 3.9e-5   1315    1.0\n",
      "u[19,3]          4.7e-5  5.5e-9 2.0e-7 4.7e-5 4.7e-5 4.7e-5 4.8e-5 4.8e-5   1315    1.0\n",
      "u[20,3]          5.9e-5  6.3e-9 2.3e-7 5.8e-5 5.8e-5 5.9e-5 5.9e-5 5.9e-5   1315    1.0\n",
      "u[21,3]          7.2e-5  7.1e-9 2.6e-7 7.1e-5 7.2e-5 7.2e-5 7.2e-5 7.2e-5   1315    1.0\n",
      "u[22,3]          8.8e-5  8.0e-9 2.9e-7 8.7e-5 8.8e-5 8.8e-5 8.8e-5 8.8e-5   1315    1.0\n",
      "u[23,3]          1.1e-4  9.0e-9 3.3e-7 1.1e-4 1.1e-4 1.1e-4 1.1e-4 1.1e-4   1314    1.0\n",
      "u[24,3]          1.3e-4  1.0e-8 3.7e-7 1.3e-4 1.3e-4 1.3e-4 1.3e-4 1.3e-4   1313    1.0\n",
      "u[25,3]          1.5e-4  1.2e-8 4.2e-7 1.5e-4 1.5e-4 1.5e-4 1.5e-4 1.6e-4   1312    1.0\n",
      "u[26,3]          1.8e-4  1.3e-8 4.7e-7 1.8e-4 1.8e-4 1.8e-4 1.8e-4 1.9e-4   1311    1.0\n",
      "u[27,3]          2.2e-4  1.5e-8 5.4e-7 2.2e-4 2.2e-4 2.2e-4 2.2e-4 2.2e-4   1311    1.0\n",
      "u[28,3]          2.6e-4  1.7e-8 6.1e-7 2.6e-4 2.6e-4 2.6e-4 2.6e-4 2.6e-4   1312    1.0\n",
      "u[29,3]          3.0e-4  1.9e-8 7.0e-7 3.0e-4 3.0e-4 3.0e-4 3.0e-4 3.0e-4   1314    1.0\n",
      "u[30,3]          3.5e-4  2.2e-8 8.0e-7 3.5e-4 3.5e-4 3.5e-4 3.5e-4 3.5e-4   1317    1.0\n",
      "u[31,3]          4.0e-4  2.5e-8 9.1e-7 4.0e-4 4.0e-4 4.0e-4 4.0e-4 4.1e-4   1317    1.0\n",
      "u[32,3]          4.6e-4  2.8e-8 1.0e-6 4.6e-4 4.6e-4 4.6e-4 4.6e-4 4.6e-4   1318    1.0\n",
      "u[33,3]          5.3e-4  3.2e-8 1.2e-6 5.2e-4 5.3e-4 5.3e-4 5.3e-4 5.3e-4   1321    1.0\n",
      "u[34,3]          6.0e-4  3.6e-8 1.3e-6 5.9e-4 6.0e-4 6.0e-4 6.0e-4 6.0e-4   1324    1.0\n",
      "u[35,3]          6.7e-4  4.0e-8 1.5e-6 6.7e-4 6.7e-4 6.7e-4 6.7e-4 6.7e-4   1328    1.0\n",
      "u[36,3]          7.5e-4  4.5e-8 1.6e-6 7.5e-4 7.5e-4 7.5e-4 7.5e-4 7.5e-4   1334    1.0\n",
      "u[37,3]          8.3e-4  5.0e-8 1.8e-6 8.3e-4 8.3e-4 8.3e-4 8.4e-4 8.4e-4   1339    1.0\n",
      "u[38,3]          9.2e-4  5.5e-8 2.0e-6 9.2e-4 9.2e-4 9.2e-4 9.3e-4 9.3e-4   1346    1.0\n",
      "u[39,3]          1.0e-3  6.0e-8 2.2e-6 1.0e-3 1.0e-3 1.0e-3 1.0e-3 1.0e-3   1352    1.0\n",
      "u[40,3]          1.1e-3  6.6e-8 2.4e-6 1.1e-3 1.1e-3 1.1e-3 1.1e-3 1.1e-3   1359    1.0\n",
      "u[41,3]          1.2e-3  7.1e-8 2.6e-6 1.2e-3 1.2e-3 1.2e-3 1.2e-3 1.2e-3   1367    1.0\n",
      "u[42,3]          1.3e-3  7.8e-8 2.9e-6 1.3e-3 1.3e-3 1.3e-3 1.3e-3 1.3e-3   1374    1.0\n",
      "u[43,3]          1.4e-3  8.4e-8 3.1e-6 1.4e-3 1.4e-3 1.4e-3 1.4e-3 1.4e-3   1381    1.0\n",
      "u[44,3]          1.5e-3  9.0e-8 3.4e-6 1.5e-3 1.5e-3 1.5e-3 1.5e-3 1.6e-3   1388    1.0\n",
      "u[45,3]          1.7e-3  9.7e-8 3.6e-6 1.7e-3 1.7e-3 1.7e-3 1.7e-3 1.7e-3   1396    1.0\n",
      "u[46,3]          1.8e-3  1.0e-7 3.9e-6 1.8e-3 1.8e-3 1.8e-3 1.8e-3 1.8e-3   1403    1.0\n",
      "u[47,3]          1.9e-3  1.1e-7 4.2e-6 1.9e-3 1.9e-3 1.9e-3 1.9e-3 1.9e-3   1409    1.0\n",
      "u[48,3]          2.0e-3  1.2e-7 4.5e-6 2.0e-3 2.0e-3 2.0e-3 2.0e-3 2.0e-3   1416    1.0\n",
      "u[1,4]           5.0e-3  1.1e-6 4.0e-5 4.9e-3 5.0e-3 5.0e-3 5.0e-3 5.1e-3   1392    1.0\n",
      "u[2,4]           6.2e-3  1.2e-6 4.6e-5 6.1e-3 6.2e-3 6.2e-3 6.2e-3 6.3e-3   1404    1.0\n",
      "u[3,4]           7.7e-3  1.4e-6 5.4e-5 7.6e-3 7.6e-3 7.7e-3 7.7e-3 7.8e-3   1419    1.0\n",
      "u[4,4]           9.5e-3  1.6e-6 6.2e-5 9.4e-3 9.5e-3 9.5e-3 9.5e-3 9.6e-3   1437    1.0\n",
      "u[5,4]             0.01  1.9e-6 7.1e-5   0.01   0.01   0.01   0.01   0.01   1461    1.0\n",
      "u[6,4]             0.01  2.1e-6 8.2e-5   0.01   0.01   0.01   0.01   0.01   1490    1.0\n",
      "u[7,4]             0.02  2.4e-6 9.4e-5   0.02   0.02   0.02   0.02   0.02   1525    1.0\n",
      "u[8,4]             0.02  2.7e-6 1.1e-4   0.02   0.02   0.02   0.02   0.02   1567    1.0\n",
      "u[9,4]             0.03  3.1e-6 1.2e-4   0.03   0.03   0.03   0.03   0.03   1610    1.0\n",
      "u[10,4]            0.03  3.5e-6 1.4e-4   0.03   0.03   0.03   0.03   0.03   1640    1.0\n",
      "u[11,4]            0.04  4.0e-6 1.6e-4   0.04   0.04   0.04   0.04   0.04   1669    1.0\n",
      "u[12,4]            0.05  4.7e-6 1.9e-4   0.05   0.05   0.05   0.05   0.05   1689    1.0\n",
      "u[13,4]            0.06  5.6e-6 2.3e-4   0.06   0.06   0.06   0.06   0.06   1697    1.0\n",
      "u[14,4]            0.07  6.8e-6 2.8e-4   0.07   0.07   0.07   0.08   0.08   1690    1.0\n",
      "u[15,4]            0.09  8.3e-6 3.4e-4   0.09   0.09   0.09   0.09   0.09   1669    1.0\n",
      "u[16,4]            0.11  1.0e-5 4.2e-4   0.11   0.11   0.11   0.11   0.11   1641    1.0\n",
      "u[17,4]            0.13  1.3e-5 5.1e-4   0.13   0.13   0.13   0.13   0.13   1609    1.0\n",
      "u[18,4]            0.16  1.6e-5 6.3e-4   0.15   0.16   0.16   0.16   0.16   1578    1.0\n",
      "u[19,4]            0.18  1.9e-5 7.6e-4   0.18   0.18   0.18   0.19   0.19   1550    1.0\n",
      "u[20,4]            0.22  2.3e-5 9.1e-4   0.21   0.22   0.22   0.22   0.22   1496    1.0\n",
      "u[21,4]            0.25  2.8e-5 1.1e-3   0.25   0.25   0.25   0.25   0.25   1448    1.0\n",
      "u[22,4]            0.29  3.3e-5 1.2e-3   0.29   0.29   0.29   0.29   0.29   1408    1.0\n",
      "u[23,4]            0.33  3.7e-5 1.4e-3   0.32   0.33   0.33   0.33   0.33   1375    1.0\n",
      "u[24,4]            0.37  4.1e-5 1.5e-3   0.36   0.36   0.37   0.37   0.37   1348    1.0\n",
      "u[25,4]             0.4  4.4e-5 1.6e-3    0.4    0.4    0.4   0.41   0.41   1326    1.0\n",
      "u[26,4]            0.44  4.6e-5 1.6e-3   0.44   0.44   0.44   0.44   0.45   1307    1.0\n",
      "u[27,4]            0.48  4.6e-5 1.7e-3   0.47   0.48   0.48   0.48   0.48   1292    1.0\n",
      "u[28,4]            0.51  4.6e-5 1.6e-3   0.51   0.51   0.51   0.51   0.51   1280    1.0\n",
      "u[29,4]            0.54  4.4e-5 1.6e-3   0.53   0.54   0.54   0.54   0.54   1270    1.0\n",
      "u[30,4]            0.56  4.1e-5 1.5e-3   0.56   0.56   0.56   0.56   0.56   1264    1.0\n",
      "u[31,4]            0.58  3.8e-5 1.4e-3   0.58   0.58   0.58   0.58   0.58   1260    1.0\n",
      "u[32,4]            0.59  3.4e-5 1.2e-3   0.59   0.59   0.59   0.59    0.6   1259    1.0\n",
      "u[33,4]             0.6  3.0e-5 1.1e-3    0.6    0.6    0.6    0.6   0.61   1263    1.0\n",
      "u[34,4]            0.61  2.7e-5 9.5e-4   0.61   0.61   0.61   0.61   0.61   1269    1.0\n",
      "u[35,4]            0.61  2.3e-5 8.2e-4   0.61   0.61   0.61   0.61   0.61   1273    1.0\n",
      "u[36,4]            0.61  2.0e-5 7.0e-4   0.61   0.61   0.61   0.61   0.61   1283    1.0\n",
      "u[37,4]            0.61  1.7e-5 6.1e-4   0.61   0.61   0.61   0.61   0.61   1299    1.0\n",
      "u[38,4]             0.6  1.5e-5 5.3e-4    0.6    0.6    0.6    0.6    0.6   1323    1.0\n",
      "u[39,4]             0.6  1.3e-5 4.8e-4    0.6    0.6    0.6    0.6    0.6   1352    1.0\n",
      "u[40,4]            0.59  1.2e-5 4.4e-4   0.59   0.59   0.59   0.59   0.59   1369    1.0\n",
      "u[41,4]            0.58  1.2e-5 4.3e-4   0.58   0.58   0.58   0.58   0.58   1376    1.0\n",
      "u[42,4]            0.57  1.2e-5 4.3e-4   0.57   0.57   0.57   0.57   0.57   1384    1.0\n",
      "u[43,4]            0.56  1.2e-5 4.4e-4   0.56   0.56   0.56   0.56   0.56   1391    1.0\n",
      "u[44,4]            0.54  1.2e-5 4.5e-4   0.54   0.54   0.54   0.54   0.54   1397    1.0\n",
      "u[45,4]            0.53  1.3e-5 4.7e-4   0.53   0.53   0.53   0.53   0.53   1397    1.0\n",
      "u[46,4]            0.52  1.3e-5 4.9e-4   0.52   0.52   0.52   0.52   0.52   1394    1.0\n",
      "u[47,4]            0.51  1.3e-5 5.0e-4   0.51   0.51   0.51   0.51   0.51   1392    1.0\n",
      "u[48,4]            0.49  1.4e-5 5.2e-4   0.49   0.49   0.49   0.49   0.49   1390    1.0\n",
      "u[1,5]              1.0  1.8e-7 6.7e-6    1.0    1.0    1.0    1.0    1.0   1452    1.0\n",
      "u[2,5]              1.0  3.8e-7 1.5e-5    1.0    1.0    1.0    1.0    1.0   1458    1.0\n",
      "u[3,5]              1.0  6.2e-7 2.4e-5    1.0    1.0    1.0    1.0    1.0   1472    1.0\n",
      "u[4,5]             0.99  8.8e-7 3.4e-5   0.99   0.99   0.99   0.99   0.99   1490    1.0\n",
      "u[5,5]             0.99  1.2e-6 4.6e-5   0.99   0.99   0.99   0.99   0.99   1514    1.0\n",
      "u[6,5]             0.99  1.5e-6 5.9e-5   0.99   0.99   0.99   0.99   0.99   1542    1.0\n",
      "u[7,5]             0.98  1.9e-6 7.4e-5   0.98   0.98   0.98   0.98   0.98   1575    1.0\n",
      "u[8,5]             0.98  2.3e-6 9.1e-5   0.98   0.98   0.98   0.98   0.98   1613    1.0\n",
      "u[9,5]             0.97  2.7e-6 1.1e-4   0.97   0.97   0.97   0.97   0.97   1639    1.0\n",
      "u[10,5]            0.97  3.3e-6 1.3e-4   0.97   0.97   0.97   0.97   0.97   1664    1.0\n",
      "u[11,5]            0.96  4.0e-6 1.6e-4   0.96   0.96   0.96   0.96   0.96   1685    1.0\n",
      "u[12,5]            0.95  4.8e-6 2.0e-4   0.95   0.95   0.95   0.95   0.95   1699    1.0\n",
      "u[13,5]            0.93  5.8e-6 2.4e-4   0.93   0.93   0.93   0.93   0.93   1703    1.0\n",
      "u[14,5]            0.92  7.1e-6 2.9e-4   0.92   0.92   0.92   0.92   0.92   1695    1.0\n",
      "u[15,5]             0.9  8.9e-6 3.6e-4    0.9    0.9    0.9    0.9    0.9   1675    1.0\n",
      "u[16,5]            0.88  1.1e-5 4.5e-4   0.88   0.88   0.88   0.88   0.88   1648    1.0\n",
      "u[17,5]            0.85  1.4e-5 5.5e-4   0.85   0.85   0.85   0.85   0.85   1618    1.0\n",
      "u[18,5]            0.82  1.7e-5 6.8e-4   0.82   0.82   0.82   0.82   0.82   1589    1.0\n",
      "u[19,5]            0.79  2.1e-5 8.3e-4   0.79   0.79   0.79   0.79   0.79   1553    1.0\n",
      "u[20,5]            0.75  2.6e-510.0e-4   0.75   0.75   0.75   0.75   0.75   1496    1.0\n",
      "u[21,5]            0.71  3.1e-5 1.2e-3   0.71   0.71   0.71   0.71   0.71   1448    1.0\n",
      "u[22,5]            0.66  3.6e-5 1.4e-3   0.66   0.66   0.66   0.66   0.67   1408    1.0\n",
      "u[23,5]            0.62  4.2e-5 1.5e-3   0.61   0.61   0.62   0.62   0.62   1374    1.0\n",
      "u[24,5]            0.57  4.6e-5 1.7e-3   0.56   0.56   0.57   0.57   0.57   1345    1.0\n",
      "u[25,5]            0.51  5.0e-5 1.8e-3   0.51   0.51   0.51   0.52   0.52   1321    1.0\n",
      "u[26,5]            0.46  5.3e-5 1.9e-3   0.46   0.46   0.46   0.46   0.47   1300    1.0\n",
      "u[27,5]            0.41  5.5e-5 2.0e-3   0.41   0.41   0.41   0.42   0.42   1283    1.0\n",
      "u[28,5]            0.37  5.6e-5 2.0e-3   0.36   0.37   0.37   0.37   0.37   1267    1.0\n",
      "u[29,5]            0.32  5.5e-5 2.0e-3   0.32   0.32   0.32   0.32   0.33   1254    1.0\n",
      "u[30,5]            0.28  5.4e-5 1.9e-3   0.28   0.28   0.28   0.28   0.29   1242    1.0\n",
      "u[31,5]            0.24  5.2e-5 1.8e-3   0.24   0.24   0.24   0.25   0.25   1231    1.0\n",
      "u[32,5]            0.21  4.9e-5 1.7e-3   0.21   0.21   0.21   0.21   0.22   1221    1.0\n",
      "u[33,5]            0.18  4.5e-5 1.6e-3   0.18   0.18   0.18   0.18   0.19   1212    1.0\n",
      "u[34,5]            0.16  4.2e-5 1.4e-3   0.15   0.16   0.16   0.16   0.16   1204    1.0\n",
      "u[35,5]            0.14  3.8e-5 1.3e-3   0.13   0.13   0.14   0.14   0.14   1196    1.0\n",
      "u[36,5]            0.12  3.4e-5 1.2e-3   0.11   0.12   0.12   0.12   0.12   1189    1.0\n",
      "u[37,5]             0.1  3.1e-5 1.1e-3    0.1    0.1    0.1    0.1    0.1   1184    1.0\n",
      "u[38,5]            0.09  2.8e-5 9.6e-4   0.08   0.09   0.09   0.09   0.09   1181    1.0\n",
      "u[39,5]            0.07  2.5e-5 8.6e-4   0.07   0.07   0.07   0.08   0.08   1179    1.0\n",
      "u[40,5]            0.06  2.2e-5 7.7e-4   0.06   0.06   0.06   0.06   0.07   1177    1.0\n",
      "u[41,5]            0.06  2.0e-5 6.9e-4   0.05   0.06   0.06   0.06   0.06   1175    1.0\n",
      "u[42,5]            0.05  1.8e-5 6.1e-4   0.05   0.05   0.05   0.05   0.05   1173    1.0\n",
      "u[43,5]            0.04  1.6e-5 5.5e-4   0.04   0.04   0.04   0.04   0.04   1171    1.0\n",
      "u[44,5]            0.04  1.4e-5 4.9e-4   0.04   0.04   0.04   0.04   0.04   1169    1.0\n",
      "u[45,5]            0.03  1.3e-5 4.4e-4   0.03   0.03   0.03   0.03   0.03   1167    1.0\n",
      "u[46,5]            0.03  1.2e-5 4.0e-4   0.03   0.03   0.03   0.03   0.03   1166    1.0\n",
      "u[47,5]            0.02  1.0e-5 3.6e-4   0.02   0.02   0.02   0.03   0.03   1164    1.0\n",
      "u[48,5]            0.02  9.4e-6 3.2e-4   0.02   0.02   0.02   0.02   0.02   1162    1.0\n",
      "u_init[1]        3.0e-7 2.6e-235.3e-23  3e-07  3e-07  3e-07  3e-07  3e-07      4   1.05\n",
      "u_init[2]           0.0     nan    0.0    0.0    0.0    0.0    0.0    0.0    nan    nan\n",
      "u_init[3]           0.0     nan    0.0    0.0    0.0    0.0    0.0    0.0    nan    nan\n",
      "u_init[4]        4.0e-3  9.2e-7 3.4e-5 4.0e-3 4.0e-3 4.0e-3 4.1e-3 4.1e-3   1383    1.0\n",
      "u_init[5]           1.0  9.7e-9 3.6e-7    1.0    1.0    1.0    1.0    1.0   1426    1.0\n",
      "R_0                7.79  6.5e-4   0.02   7.74   7.77   7.79   7.81   7.84   1368    1.0\n",
      "ll_lambda[1,1]    43.01    0.01   0.38  42.28  42.75   43.0  43.27  43.76   1356    1.0\n",
      "ll_lambda[2,1]    91.37    0.02    0.8  89.82  90.82  91.36  91.92  92.98   1358    1.0\n",
      "ll_lambda[3,1]   150.12    0.03   1.29 147.62 149.23  150.1  151.0  152.7   1361    1.0\n",
      "ll_lambda[4,1]   221.72    0.05   1.84 218.14 220.45 221.69 222.97 225.38   1363    1.0\n",
      "ll_lambda[5,1]    309.2    0.07   2.46 304.42  307.5 309.16 310.89 314.03   1367    1.0\n",
      "ll_lambda[6,1]   416.31    0.09   3.15  410.2 414.16 416.29 418.48 422.44   1371    1.0\n",
      "ll_lambda[7,1]   547.61    0.11   3.94 540.03 544.87 547.56 550.29 555.23   1377    1.0\n",
      "ll_lambda[8,1]   708.62    0.13    4.8 699.44 705.28 708.54 711.91 717.97   1383    1.0\n",
      "ll_lambda[9,1]   906.12    0.15   5.76 895.02 902.12 906.04 910.04 917.42   1391    1.0\n",
      "ll_lambda[10,1]  1148.2    0.18    6.8 1134.9 1143.4 1148.1 1152.8 1161.7   1401    1.0\n",
      "ll_lambda[11,1]  1444.6    0.21   7.93 1429.1 1439.0 1444.4 1450.1 1460.2   1413    1.0\n",
      "ll_lambda[12,1]  1806.9    0.24   9.13 1789.2 1800.6 1806.8 1813.2 1825.0   1427    1.0\n",
      "ll_lambda[13,1]  2248.7    0.27  10.39 2228.3 2241.6 2248.7 2255.9 2269.6   1445    1.0\n",
      "ll_lambda[14,1]  2785.9    0.31  11.69 2763.1 2778.0 2785.9 2794.0 2809.6   1467    1.0\n",
      "ll_lambda[15,1]  3436.6    0.34  13.02 3411.0 3427.6 3436.5 3445.4 3463.0   1493    1.0\n",
      "ll_lambda[16,1]  4221.0    0.37  14.36 4192.2 4211.1 4221.0 4230.7 4250.1   1524    1.0\n",
      "ll_lambda[17,1]  5161.4     0.4  15.72 5129.9 5150.8 5161.4 5172.0 5192.8   1559    1.0\n",
      "ll_lambda[18,1]  6281.4    0.43  17.14 6247.1 6269.8 6281.2 6292.6 6315.1   1594    1.0\n",
      "ll_lambda[19,1]  7604.9    0.46  18.67 7567.3 7592.6 7604.6 7617.4 7641.3   1624    1.0\n",
      "ll_lambda[20,1]  9155.1    0.51  20.44 9114.1 9141.4 9154.9 9169.1 9195.1   1636    1.0\n",
      "ll_lambda[21,1]   1.1e4    0.56  22.58  1.1e4  1.1e4  1.1e4  1.1e4  1.1e4   1624    1.0\n",
      "ll_lambda[22,1]   1.3e4    0.64  25.21  1.3e4  1.3e4  1.3e4  1.3e4  1.3e4   1571    1.0\n",
      "ll_lambda[23,1]   1.5e4    0.78  28.39  1.5e4  1.5e4  1.5e4  1.5e4  1.5e4   1334    1.0\n",
      "ll_lambda[24,1]   1.8e4    0.92  32.07  1.8e4  1.8e4  1.8e4  1.8e4  1.8e4   1202   1.01\n",
      "ll_lambda[25,1]   2.1e4    1.07  36.11  2.1e4  2.1e4  2.1e4  2.1e4  2.1e4   1134   1.01\n",
      "ll_lambda[26,1]   2.4e4    1.22  40.27  2.4e4  2.4e4  2.4e4  2.4e4  2.4e4   1090   1.01\n",
      "ll_lambda[27,1]   2.7e4    1.37  44.27  2.7e4  2.7e4  2.7e4  2.7e4  2.7e4   1047   1.01\n",
      "ll_lambda[28,1]   3.1e4    1.47  47.84  3.1e4  3.1e4  3.1e4  3.1e4  3.1e4   1056   1.01\n",
      "ll_lambda[29,1]   3.5e4    1.56  50.76  3.5e4  3.5e4  3.5e4  3.5e4  3.5e4   1056   1.01\n",
      "ll_lambda[30,1]   3.8e4    1.63  52.88  3.8e4  3.8e4  3.8e4  3.8e4  3.9e4   1049   1.01\n",
      "ll_lambda[31,1]   4.2e4    1.69  54.15  4.2e4  4.2e4  4.2e4  4.2e4  4.2e4   1027   1.01\n",
      "ll_lambda[32,1]   4.6e4    1.72  54.64  4.6e4  4.6e4  4.6e4  4.6e4  4.6e4   1008   1.01\n",
      "ll_lambda[33,1]   5.0e4    1.73  54.51  5.0e4  5.0e4  5.0e4  5.0e4  5.0e4    997   1.01\n",
      "ll_lambda[34,1]   5.4e4    1.71  54.03  5.4e4  5.4e4  5.4e4  5.4e4  5.4e4    998   1.01\n",
      "ll_lambda[35,1]   5.8e4    1.68  53.57  5.8e4  5.8e4  5.8e4  5.8e4  5.8e4   1016   1.01\n",
      "ll_lambda[36,1]   6.2e4    1.65  53.51  6.1e4  6.2e4  6.2e4  6.2e4  6.2e4   1052   1.01\n",
      "ll_lambda[37,1]   6.5e4    1.63  54.24  6.5e4  6.5e4  6.5e4  6.5e4  6.5e4   1112   1.01\n",
      "ll_lambda[38,1]   6.9e4    1.62  56.06  6.8e4  6.9e4  6.9e4  6.9e4  6.9e4   1199   1.01\n",
      "ll_lambda[39,1]   7.2e4    1.64  59.11  7.2e4  7.2e4  7.2e4  7.2e4  7.2e4   1305    1.0\n",
      "ll_lambda[40,1]   7.5e4    1.68  63.38  7.5e4  7.5e4  7.5e4  7.5e4  7.5e4   1417    1.0\n",
      "ll_lambda[41,1]   7.8e4    1.76  68.73  7.8e4  7.8e4  7.8e4  7.8e4  7.8e4   1518    1.0\n",
      "ll_lambda[42,1]   8.1e4    1.87  74.95  8.0e4  8.1e4  8.1e4  8.1e4  8.1e4   1609    1.0\n",
      "ll_lambda[43,1]   8.3e4    2.02  81.82  8.3e4  8.3e4  8.3e4  8.3e4  8.3e4   1646    1.0\n",
      "ll_lambda[44,1]   8.6e4    2.19  89.15  8.5e4  8.5e4  8.6e4  8.6e4  8.6e4   1661    1.0\n",
      "ll_lambda[45,1]   8.8e4    2.37  96.76  8.8e4  8.8e4  8.8e4  8.8e4  8.8e4   1667    1.0\n",
      "ll_lambda[46,1]   9.0e4    2.56  104.5  9.0e4  9.0e4  9.0e4  9.0e4  9.0e4   1669    1.0\n",
      "ll_lambda[47,1]   9.2e4    2.75 112.28  9.1e4  9.2e4  9.2e4  9.2e4  9.2e4   1667    1.0\n",
      "ll_lambda[48,1]   9.3e4    2.94 119.99  9.3e4  9.3e4  9.3e4  9.3e4  9.4e4   1662    1.0\n",
      "ll_lambda[1,2]     0.39  9.0e-5 3.3e-3   0.38   0.39   0.39   0.39   0.39   1366    1.0\n",
      "ll_lambda[2,2]     1.54  3.7e-4   0.01   1.51   1.53   1.54   1.55   1.57   1366    1.0\n",
      "ll_lambda[3,2]     3.62  8.8e-4   0.03   3.55    3.6   3.62   3.64   3.68   1367    1.0\n",
      "ll_lambda[4,2]     6.82  1.6e-3   0.06    6.7   6.78   6.82   6.86   6.94   1369    1.0\n",
      "ll_lambda[5,2]     11.4  2.7e-3    0.1   11.2  11.33   11.4  11.46  11.59   1371    1.0\n",
      "ll_lambda[6,2]    17.66  4.0e-3   0.15  17.37  17.56  17.66  17.76  17.95   1373    1.0\n",
      "ll_lambda[7,2]    25.98  5.7e-3   0.21  25.56  25.84  25.98  26.12  26.39   1376    1.0\n",
      "ll_lambda[8,2]    36.82  7.9e-3   0.29  36.25  36.63  36.82  37.02  37.39   1379    1.0\n",
      "ll_lambda[9,2]    50.77    0.01   0.39  50.01   50.5  50.77  51.04  51.51   1382    1.0\n",
      "ll_lambda[10,2]   68.51    0.01    0.5  67.53  68.17  68.51  68.86  69.47   1387    1.0\n",
      "ll_lambda[11,2]   90.91    0.02   0.63  89.67  90.48  90.91  91.35  92.13   1392    1.0\n",
      "ll_lambda[12,2]   119.0    0.02   0.79 117.45 118.46  119.0 119.55 120.51   1398    1.0\n",
      "ll_lambda[13,2]  154.04    0.03   0.97 152.13 153.39 154.03 154.72 155.88   1404    1.0\n",
      "ll_lambda[14,2]  197.55    0.03   1.17 195.26 196.76 197.53 198.37 199.78   1412    1.0\n",
      "ll_lambda[15,2]  251.33    0.04   1.39 248.59  250.4 251.32  252.3 253.97   1422    1.0\n",
      "ll_lambda[16,2]  317.52    0.04   1.64 314.26 316.45 317.51 318.68 320.64   1432    1.0\n",
      "ll_lambda[17,2]  398.64    0.05   1.91  394.8  397.4 398.62 399.96 402.28   1444    1.0\n",
      "ll_lambda[18,2]  497.59    0.06   2.21  493.2 496.17 497.55 499.11 501.79   1458    1.0\n",
      "ll_lambda[19,2]  617.69    0.07   2.52  612.7 616.04  617.6 619.48 622.52   1473    1.0\n",
      "ll_lambda[20,2]  762.68    0.07   2.86 757.01 760.82 762.61 764.65 768.18   1489    1.0\n",
      "ll_lambda[21,2]  936.67    0.08   3.22 930.32 934.53 936.69 938.84 942.88   1503    1.0\n",
      "ll_lambda[22,2]  1144.1    0.09   3.61 1137.0 1141.7 1144.1 1146.5 1151.0   1508    1.0\n",
      "ll_lambda[23,2]  1389.6     0.1   4.03 1381.8 1387.0 1389.6 1392.3 1397.6   1510    1.0\n",
      "ll_lambda[24,2]  1678.1    0.12    4.5 1669.4 1675.1 1678.1 1681.1 1687.1   1508    1.0\n",
      "ll_lambda[25,2]  2014.2    0.13   5.04 2004.4 2010.8 2014.2 2017.6 2024.4   1500    1.0\n",
      "ll_lambda[26,2]  2402.5    0.15   5.65 2391.4 2398.8 2402.6 2406.4 2413.6   1488    1.0\n",
      "ll_lambda[27,2]  2847.3    0.17   6.36 2834.7 2843.0 2847.3 2851.6 2860.0   1471    1.0\n",
      "ll_lambda[28,2]  3352.1    0.19   7.19 3337.8 3347.3 3352.1 3356.9 3366.3   1452    1.0\n",
      "ll_lambda[29,2]  3920.1    0.22   8.15 3903.9 3914.6 3920.0 3925.6 3935.5   1432    1.0\n",
      "ll_lambda[30,2]  4553.6    0.25   9.24 4534.8 4547.4 4553.5 4559.8 4571.2   1412    1.0\n",
      "ll_lambda[31,2]  5254.3    0.28  10.47 5233.2 5247.2 5254.3 5261.4 5274.5   1395    1.0\n",
      "ll_lambda[32,2]  6023.0    0.32  11.83 5998.9 6015.1 6023.0 6031.0 6045.5   1380    1.0\n",
      "ll_lambda[33,2]  6859.9    0.36  13.34 6833.3 6851.2 6859.9 6868.8 6885.8   1368    1.0\n",
      "ll_lambda[34,2]  7764.5    0.41  14.98 7734.5 7754.6 7764.4 7774.4 7794.0   1358    1.0\n",
      "ll_lambda[35,2]  8735.8    0.46  16.75 8702.2 8724.7 8735.7 8746.7 8769.4   1351    1.0\n",
      "ll_lambda[36,2]  9772.0    0.51  18.66 9734.4 9759.6 9772.0 9784.3 9810.2   1347    1.0\n",
      "ll_lambda[37,2]   1.1e4    0.56  20.69  1.1e4  1.1e4  1.1e4  1.1e4  1.1e4   1345    1.0\n",
      "ll_lambda[38,2]   1.2e4    0.62  22.85  1.2e4  1.2e4  1.2e4  1.2e4  1.2e4   1344    1.0\n",
      "ll_lambda[39,2]   1.3e4    0.69  25.14  1.3e4  1.3e4  1.3e4  1.3e4  1.3e4   1345    1.0\n",
      "ll_lambda[40,2]   1.5e4    0.75  27.56  1.4e4  1.5e4  1.5e4  1.5e4  1.5e4   1347    1.0\n",
      "ll_lambda[41,2]   1.6e4    0.82  30.11  1.6e4  1.6e4  1.6e4  1.6e4  1.6e4   1351    1.0\n",
      "ll_lambda[42,2]   1.7e4    0.89  32.78  1.7e4  1.7e4  1.7e4  1.7e4  1.7e4   1355    1.0\n",
      "ll_lambda[43,2]   1.9e4    0.96  35.58  1.9e4  1.9e4  1.9e4  1.9e4  1.9e4   1360    1.0\n",
      "ll_lambda[44,2]   2.0e4    1.04   38.5  2.0e4  2.0e4  2.0e4  2.0e4  2.0e4   1365    1.0\n",
      "ll_lambda[45,2]   2.2e4    1.12  41.53  2.2e4  2.2e4  2.2e4  2.2e4  2.2e4   1370    1.0\n",
      "ll_lambda[46,2]   2.3e4     1.2  44.69  2.3e4  2.3e4  2.3e4  2.3e4  2.3e4   1376    1.0\n",
      "ll_lambda[47,2]   2.5e4    1.29  47.95  2.5e4  2.5e4  2.5e4  2.5e4  2.5e4   1382    1.0\n",
      "ll_lambda[48,2]   2.6e4    1.38  51.31  2.6e4  2.6e4  2.6e4  2.6e4  2.6e4   1388    1.0\n",
      "ll_lambda[1,3]      0.3  7.1e-5 2.6e-3   0.29    0.3    0.3    0.3    0.3   1304    1.0\n",
      "ll_lambda[2,3]     1.18  2.9e-4   0.01   1.16   1.18   1.18   1.19    1.2   1312    1.0\n",
      "ll_lambda[3,3]     2.78  6.9e-4   0.03   2.73   2.76   2.78   2.79   2.83   1312    1.0\n",
      "ll_lambda[4,3]     5.24  1.3e-3   0.05   5.15   5.21   5.24   5.27   5.33   1312    1.0\n",
      "ll_lambda[5,3]     8.75  2.1e-3   0.08   8.61    8.7   8.75   8.81    8.9   1312    1.0\n",
      "ll_lambda[6,3]    13.56  3.2e-3   0.12  13.34  13.48  13.56  13.64  13.78   1312    1.0\n",
      "ll_lambda[7,3]    19.95  4.5e-3   0.16  19.64  19.84  19.95  20.06  20.27   1312    1.0\n",
      "ll_lambda[8,3]    28.28  6.2e-3   0.23  27.85  28.13  28.27  28.43  28.71   1312    1.0\n",
      "ll_lambda[9,3]    38.99  8.3e-3    0.3  38.41  38.78  38.98  39.19  39.56   1312    1.0\n",
      "ll_lambda[10,3]   52.61    0.01   0.39  51.87  52.35  52.61  52.87  53.36   1312    1.0\n",
      "ll_lambda[11,3]   69.81    0.01   0.49  68.87  69.48   69.8  70.14  70.76   1312    1.0\n",
      "ll_lambda[12,3]   91.39    0.02   0.61  90.21  90.97  91.37   91.8  92.57   1312    1.0\n",
      "ll_lambda[13,3]   118.3    0.02   0.75 116.85 117.78 118.28 118.81 119.77   1312    1.0\n",
      "ll_lambda[14,3]  151.71    0.02   0.91 149.94 151.09 151.68 152.33 153.47   1313    1.0\n",
      "ll_lambda[15,3]  193.01    0.03   1.08 190.88 192.28 192.97 193.75  195.1   1313    1.0\n",
      "ll_lambda[16,3]  243.84    0.04   1.28 241.34 242.99  243.8 244.72 246.32   1314    1.0\n",
      "ll_lambda[17,3]  306.14    0.04   1.49 303.16 305.12 306.07 307.17 309.07   1314    1.0\n",
      "ll_lambda[18,3]  382.12    0.05   1.73 378.69 380.94 382.07 383.31 385.54   1315    1.0\n",
      "ll_lambda[19,3]  474.36    0.05   1.99 470.47  473.0  474.3 475.72 478.33   1315    1.0\n",
      "ll_lambda[20,3]   585.7    0.06   2.27 581.18 584.15 585.65 587.26 590.25   1315    1.0\n",
      "ll_lambda[21,3]  719.32    0.07   2.57 714.17 717.58 719.25 721.09 724.55   1315    1.0\n",
      "ll_lambda[22,3]  878.62    0.08   2.91 872.87 876.61 878.53 880.63 884.44   1315    1.0\n",
      "ll_lambda[23,3]  1067.2    0.09   3.28 1060.6 1064.9 1067.1 1069.4 1073.7   1314    1.0\n",
      "ll_lambda[24,3]  1288.7     0.1    3.7 1281.3 1286.2 1288.6 1291.2 1295.9   1313    1.0\n",
      "ll_lambda[25,3]  1546.8    0.12   4.18 1538.4 1544.0 1546.7 1549.5 1554.9   1312    1.0\n",
      "ll_lambda[26,3]  1845.0    0.13   4.74 1835.6 1841.8 1845.0 1848.2 1854.1   1311    1.0\n",
      "ll_lambda[27,3]  2186.6    0.15   5.39 2176.0 2182.9 2186.5 2190.3 2196.8   1311    1.0\n",
      "ll_lambda[28,3]  2574.3    0.17   6.14 2562.3 2570.0 2574.1 2578.5 2586.0   1312    1.0\n",
      "ll_lambda[29,3]  3010.5    0.19   7.01 2997.0 3005.6 3010.2 3015.4 3023.8   1314    1.0\n",
      "ll_lambda[30,3]  3497.0    0.22    8.0 3481.8 3491.4 3496.6 3502.7 3512.0   1317    1.0\n",
      "ll_lambda[31,3]  4035.1    0.25    9.1 4017.7 4028.9 4034.7 4041.6 4052.3   1317    1.0\n",
      "ll_lambda[32,3]  4625.4    0.28  10.32 4605.3 4618.3 4624.9 4632.8 4645.0   1318    1.0\n",
      "ll_lambda[33,3]  5268.1    0.32  11.67 5245.2 5260.0 5267.6 5276.4 5289.8   1321    1.0\n",
      "ll_lambda[34,3]  5962.8    0.36  13.13 5937.2 5953.8 5962.2 5972.0 5987.1   1324    1.0\n",
      "ll_lambda[35,3]  6708.7     0.4   14.7 6679.8 6698.6 6708.1 6719.0 6735.9   1328    1.0\n",
      "ll_lambda[36,3]  7504.5    0.45  16.39 7472.5 7493.3 7504.1 7515.7 7535.1   1334    1.0\n",
      "ll_lambda[37,3]  8348.7     0.5  18.18 8313.3 8336.1 8348.3 8360.9 8382.9   1339    1.0\n",
      "ll_lambda[38,3]  9239.5    0.55  20.08 9200.9 9225.5 9239.2 9253.3 9277.7   1346    1.0\n",
      "ll_lambda[39,3]   1.0e4     0.6  22.09  1.0e4  1.0e4  1.0e4  1.0e4  1.0e4   1352    1.0\n",
      "ll_lambda[40,3]   1.1e4    0.66  24.21  1.1e4  1.1e4  1.1e4  1.1e4  1.1e4   1359    1.0\n",
      "ll_lambda[41,3]   1.2e4    0.71  26.43  1.2e4  1.2e4  1.2e4  1.2e4  1.2e4   1367    1.0\n",
      "ll_lambda[42,3]   1.3e4    0.78  28.75  1.3e4  1.3e4  1.3e4  1.3e4  1.3e4   1374    1.0\n",
      "ll_lambda[43,3]   1.4e4    0.84  31.17  1.4e4  1.4e4  1.4e4  1.4e4  1.4e4   1381    1.0\n",
      "ll_lambda[44,3]   1.5e4     0.9  33.69  1.5e4  1.5e4  1.5e4  1.5e4  1.6e4   1388    1.0\n",
      "ll_lambda[45,3]   1.7e4    0.97  36.29  1.7e4  1.7e4  1.7e4  1.7e4  1.7e4   1396    1.0\n",
      "ll_lambda[46,3]   1.8e4    1.04  38.99  1.8e4  1.8e4  1.8e4  1.8e4  1.8e4   1403    1.0\n",
      "ll_lambda[47,3]   1.9e4    1.11  41.78  1.9e4  1.9e4  1.9e4  1.9e4  1.9e4   1409    1.0\n",
      "ll_lambda[48,3]   2.0e4    1.19  44.64  2.0e4  2.0e4  2.0e4  2.0e4  2.0e4   1416    1.0\n",
      "ll_[1]            -34.2  9.7e-3   0.36 -34.91 -34.45  -34.2 -33.96 -33.51   1356    1.0\n",
      "ll_[2]           -47.49    0.02   0.65 -48.79 -47.93 -47.48 -47.04 -46.23   1358    1.0\n",
      "ll_[3]           -42.74    0.02   0.81 -44.37  -43.3 -42.73 -42.18 -41.18   1360    1.0\n",
      "ll_[4]           -22.27    0.02   0.66 -23.59 -22.72 -22.25 -21.81 -21.02   1363    1.0\n",
      "ll_[5]           -28.66    0.02    0.8 -30.27 -29.21 -28.65  -28.1 -27.15   1367    1.0\n",
      "ll_[6]           -35.79    0.03   0.96 -37.72 -36.45 -35.78 -35.12 -33.97   1370    1.0\n",
      "ll_[7]           -37.78    0.03   1.03 -39.83 -38.47 -37.75 -37.05  -35.8   1372    1.0\n",
      "ll_[8]           -22.76    0.02   0.81 -24.39  -23.3 -22.73 -22.19 -21.24   1376    1.0\n",
      "ll_[9]           -18.93    0.02   0.71 -20.35  -19.4  -18.9 -18.42 -17.61   1374    1.0\n",
      "ll_[10]          -24.45    0.02   0.89 -26.22 -25.05 -24.42 -23.82 -22.78   1379    1.0\n",
      "ll_[11]          -28.04    0.01   0.46 -28.98 -28.34 -28.01 -27.71 -27.22   1494    1.0\n",
      "ll_[12]           -24.8  3.5e-3   0.14 -25.11 -24.89  -24.8 -24.71 -24.56   1539    1.0\n",
      "ll_[13]          -18.93  4.6e-3   0.17  -19.3 -19.03 -18.91 -18.81 -18.66   1301    1.0\n",
      "ll_[14]          -34.24  6.7e-3   0.25 -34.74 -34.41 -34.24 -34.07  -33.8   1339    1.0\n",
      "ll_[15]          -64.94    0.01    0.4 -65.76  -65.2 -64.92 -64.67  -64.2   1335    1.0\n",
      "ll_[16]          -84.33    0.02   0.62 -85.64 -84.74 -84.31 -83.91 -83.15   1309    1.0\n",
      "ll_[17]          -63.05    0.01   0.49 -64.06 -63.37 -63.02 -62.71 -62.11   1381    1.0\n",
      "ll_[18]          -28.97    0.02   0.73 -30.47 -29.42 -28.95 -28.46 -27.64   1521    1.0\n",
      "ll_[19]          -31.79    0.03   1.23 -34.38 -32.58 -31.77 -30.98 -29.39   1603    1.0\n",
      "ll_[20]          -23.77    0.02    0.8 -25.44  -24.3 -23.75 -23.22 -22.28   1651    1.0\n",
      "ll_[21]          -34.06    0.02   0.69 -35.46 -34.51 -34.06  -33.6 -32.75   1387    1.0\n",
      "ll_[22]          -261.5    0.12   4.91 -271.2 -264.8 -261.5 -258.1 -251.8   1595    1.0\n",
      "ll_[23]          -38.27    0.02   0.81  -39.9 -38.82 -38.27 -37.72 -36.73   1323    1.0\n",
      "ll_[24]          -48.43    0.03   1.01 -50.48  -49.1  -48.4 -47.75 -46.53   1382    1.0\n",
      "ll_[25]          -62.14    0.03   1.24 -64.56 -62.95 -62.12 -61.26 -59.74   1374    1.0\n",
      "ll_[26]           -82.0    0.05   1.77 -85.64 -83.18 -81.99 -80.79 -78.62   1267    1.0\n",
      "ll_[27]          -69.72    0.06   2.07 -74.01 -71.09  -69.7 -68.38 -65.71   1190   1.01\n",
      "ll_[28]          -189.4     0.1   3.52 -196.5 -191.7 -189.4 -187.0 -182.6   1210    1.0\n",
      "ll_[29]          -104.1    0.07   2.32 -108.9 -105.6 -104.0 -102.5 -99.74   1235    1.0\n",
      "ll_[30]          -56.97    0.03   1.23 -59.47 -57.81 -56.98  -56.1  -54.6   1312    1.0\n",
      "ll_[31]          -151.2    0.07    2.7 -156.5 -153.0 -151.2 -149.3 -146.0   1323    1.0\n",
      "ll_[32]          -170.9    0.08   3.01 -176.9 -172.9 -170.9 -168.8 -165.1   1312    1.0\n",
      "ll_[33]          -81.78    0.06   2.07 -85.81 -83.15 -81.81 -80.32 -77.82   1293    1.0\n",
      "ll_[34]           -95.9    0.06   2.25 -100.3 -97.44 -95.87 -94.38 -91.62   1307    1.0\n",
      "ll_[35]          -85.73    0.06   2.17 -90.07  -87.2 -85.66 -84.18 -81.72   1317    1.0\n",
      "ll_[36]          -68.93    0.06   2.06 -73.09 -70.32 -68.87 -67.55 -64.89   1298    1.0\n",
      "ll_[37]           -65.7    0.06    2.1 -69.75 -67.09 -65.73 -64.28 -61.61   1372    1.0\n",
      "ll_[38]          -71.06    0.06    2.3 -75.51 -72.61 -71.02 -69.53 -66.63   1364    1.0\n",
      "ll_[39]          -65.69    0.06   2.19 -69.98 -67.26 -65.62 -64.23 -61.49   1489    1.0\n",
      "ll_[40]          -29.02    0.03   1.12 -31.26 -29.76 -29.01 -28.22 -26.98   1445    1.0\n",
      "ll_[41]          -21.52    0.02   0.62 -22.84 -21.92  -21.5 -21.09 -20.39   1313    1.0\n",
      "ll_[42]          -22.42    0.02   0.75  -24.0 -22.89 -22.42 -21.91 -21.03   1338    1.0\n",
      "ll_[43]          -27.57    0.03   1.17 -29.92 -28.34 -27.58 -26.75 -25.39   1374    1.0\n",
      "ll_[44]          -40.46    0.05   1.85 -44.05 -41.73 -40.45 -39.17 -36.88   1400    1.0\n",
      "ll_[45]          -75.69    0.08   3.04  -81.4 -77.74 -75.73 -73.58 -69.97   1358    1.0\n",
      "ll_[46]          -173.9    0.14   5.18 -184.0 -177.4 -174.0 -170.3 -163.7   1355    1.0\n",
      "ll_[47]          -272.7    0.19   6.86 -285.7 -277.3 -272.8 -268.0 -259.2   1365    1.0\n",
      "ll_[48]          -343.6    0.22   8.06 -358.9 -349.1 -343.6 -338.1 -328.1   1396    1.0\n",
      "u_predict[1,1]   4.3e-6  1.0e-9 3.8e-8 4.2e-6 4.3e-6 4.3e-6 4.3e-6 4.4e-6   1356    1.0\n",
      "u_predict[2,1]   9.1e-6  2.2e-9 8.0e-8 9.0e-6 9.1e-6 9.1e-6 9.2e-6 9.3e-6   1358    1.0\n",
      "u_predict[3,1]   1.5e-5  3.5e-9 1.3e-7 1.5e-5 1.5e-5 1.5e-5 1.5e-5 1.5e-5   1361    1.0\n",
      "u_predict[4,1]   2.2e-5  5.0e-9 1.8e-7 2.2e-5 2.2e-5 2.2e-5 2.2e-5 2.3e-5   1363    1.0\n",
      "u_predict[5,1]   3.1e-5  6.6e-9 2.5e-7 3.0e-5 3.1e-5 3.1e-5 3.1e-5 3.1e-5   1367    1.0\n",
      "u_predict[6,1]   4.2e-5  8.5e-9 3.2e-7 4.1e-5 4.1e-5 4.2e-5 4.2e-5 4.2e-5   1371    1.0\n",
      "u_predict[7,1]   5.5e-5  1.1e-8 3.9e-7 5.4e-5 5.4e-5 5.5e-5 5.5e-5 5.6e-5   1377    1.0\n",
      "u_predict[8,1]   7.1e-5  1.3e-8 4.8e-7 7.0e-5 7.1e-5 7.1e-5 7.1e-5 7.2e-5   1383    1.0\n",
      "u_predict[9,1]   9.1e-5  1.5e-8 5.8e-7 9.0e-5 9.0e-5 9.1e-5 9.1e-5 9.2e-5   1391    1.0\n",
      "u_predict[10,1]  1.1e-4  1.8e-8 6.8e-7 1.1e-4 1.1e-4 1.1e-4 1.2e-4 1.2e-4   1401    1.0\n",
      "u_predict[11,1]  1.4e-4  2.1e-8 7.9e-7 1.4e-4 1.4e-4 1.4e-4 1.5e-4 1.5e-4   1413    1.0\n",
      "u_predict[12,1]  1.8e-4  2.4e-8 9.1e-7 1.8e-4 1.8e-4 1.8e-4 1.8e-4 1.8e-4   1427    1.0\n",
      "u_predict[13,1]  2.2e-4  2.7e-8 1.0e-6 2.2e-4 2.2e-4 2.2e-4 2.3e-4 2.3e-4   1445    1.0\n",
      "u_predict[14,1]  2.8e-4  3.1e-8 1.2e-6 2.8e-4 2.8e-4 2.8e-4 2.8e-4 2.8e-4   1467    1.0\n",
      "u_predict[15,1]  3.4e-4  3.4e-8 1.3e-6 3.4e-4 3.4e-4 3.4e-4 3.4e-4 3.5e-4   1493    1.0\n",
      "u_predict[16,1]  4.2e-4  3.7e-8 1.4e-6 4.2e-4 4.2e-4 4.2e-4 4.2e-4 4.3e-4   1524    1.0\n",
      "u_predict[17,1]  5.2e-4  4.0e-8 1.6e-6 5.1e-4 5.2e-4 5.2e-4 5.2e-4 5.2e-4   1559    1.0\n",
      "u_predict[18,1]  6.3e-4  4.3e-8 1.7e-6 6.2e-4 6.3e-4 6.3e-4 6.3e-4 6.3e-4   1594    1.0\n",
      "u_predict[19,1]  7.6e-4  4.6e-8 1.9e-6 7.6e-4 7.6e-4 7.6e-4 7.6e-4 7.6e-4   1624    1.0\n",
      "u_predict[20,1]  9.2e-4  5.1e-8 2.0e-6 9.1e-4 9.1e-4 9.2e-4 9.2e-4 9.2e-4   1636    1.0\n",
      "u_predict[21,1]  1.1e-3  5.6e-8 2.3e-6 1.1e-3 1.1e-3 1.1e-3 1.1e-3 1.1e-3   1624    1.0\n",
      "u_predict[22,1]  1.3e-3  6.4e-8 2.5e-6 1.3e-3 1.3e-3 1.3e-3 1.3e-3 1.3e-3   1571    1.0\n",
      "u_predict[23,1]  1.5e-3  7.8e-8 2.8e-6 1.5e-3 1.5e-3 1.5e-3 1.5e-3 1.5e-3   1334    1.0\n",
      "u_predict[24,1]  1.8e-3  9.2e-8 3.2e-6 1.8e-3 1.8e-3 1.8e-3 1.8e-3 1.8e-3   1202   1.01\n",
      "u_predict[25,1]  2.1e-3  1.1e-7 3.6e-6 2.1e-3 2.1e-3 2.1e-3 2.1e-3 2.1e-3   1134   1.01\n",
      "u_predict[26,1]  2.4e-3  1.2e-7 4.0e-6 2.4e-3 2.4e-3 2.4e-3 2.4e-3 2.4e-3   1090   1.01\n",
      "u_predict[27,1]  2.7e-3  1.4e-7 4.4e-6 2.7e-3 2.7e-3 2.7e-3 2.7e-3 2.7e-3   1047   1.01\n",
      "u_predict[28,1]  3.1e-3  1.5e-7 4.8e-6 3.1e-3 3.1e-3 3.1e-3 3.1e-3 3.1e-3   1056   1.01\n",
      "u_predict[29,1]  3.5e-3  1.6e-7 5.1e-6 3.5e-3 3.5e-3 3.5e-3 3.5e-3 3.5e-3   1056   1.01\n",
      "u_predict[30,1]  3.8e-3  1.6e-7 5.3e-6 3.8e-3 3.8e-3 3.8e-3 3.8e-3 3.9e-3   1049   1.01\n",
      "u_predict[31,1]  4.2e-3  1.7e-7 5.4e-6 4.2e-3 4.2e-3 4.2e-3 4.2e-3 4.2e-3   1027   1.01\n",
      "u_predict[32,1]  4.6e-3  1.7e-7 5.5e-6 4.6e-3 4.6e-3 4.6e-3 4.6e-3 4.6e-3   1008   1.01\n",
      "u_predict[33,1]  5.0e-3  1.7e-7 5.5e-6 5.0e-3 5.0e-3 5.0e-3 5.0e-3 5.0e-3    997   1.01\n",
      "u_predict[34,1]  5.4e-3  1.7e-7 5.4e-6 5.4e-3 5.4e-3 5.4e-3 5.4e-3 5.4e-3    998   1.01\n",
      "u_predict[35,1]  5.8e-3  1.7e-7 5.4e-6 5.8e-3 5.8e-3 5.8e-3 5.8e-3 5.8e-3   1016   1.01\n",
      "u_predict[36,1]  6.2e-3  1.6e-7 5.4e-6 6.1e-3 6.2e-3 6.2e-3 6.2e-3 6.2e-3   1052   1.01\n",
      "u_predict[37,1]  6.5e-3  1.6e-7 5.4e-6 6.5e-3 6.5e-3 6.5e-3 6.5e-3 6.5e-3   1112   1.01\n",
      "u_predict[38,1]  6.9e-3  1.6e-7 5.6e-6 6.8e-3 6.9e-3 6.9e-3 6.9e-3 6.9e-3   1199   1.01\n",
      "u_predict[39,1]  7.2e-3  1.6e-7 5.9e-6 7.2e-3 7.2e-3 7.2e-3 7.2e-3 7.2e-3   1305    1.0\n",
      "u_predict[40,1]  7.5e-3  1.7e-7 6.3e-6 7.5e-3 7.5e-3 7.5e-3 7.5e-3 7.5e-3   1417    1.0\n",
      "u_predict[41,1]  7.8e-3  1.8e-7 6.9e-6 7.8e-3 7.8e-3 7.8e-3 7.8e-3 7.8e-3   1518    1.0\n",
      "u_predict[42,1]  8.1e-3  1.9e-7 7.5e-6 8.0e-3 8.1e-3 8.1e-3 8.1e-3 8.1e-3   1609    1.0\n",
      "u_predict[43,1]  8.3e-3  2.0e-7 8.2e-6 8.3e-3 8.3e-3 8.3e-3 8.3e-3 8.3e-3   1646    1.0\n",
      "u_predict[44,1]  8.6e-3  2.2e-7 8.9e-6 8.5e-3 8.5e-3 8.6e-3 8.6e-3 8.6e-3   1661    1.0\n",
      "u_predict[45,1]  8.8e-3  2.4e-7 9.7e-6 8.8e-3 8.8e-3 8.8e-3 8.8e-3 8.8e-3   1667    1.0\n",
      "u_predict[46,1]  9.0e-3  2.6e-7 1.0e-5 9.0e-3 9.0e-3 9.0e-3 9.0e-3 9.0e-3   1669    1.0\n",
      "u_predict[47,1]  9.2e-3  2.8e-7 1.1e-5 9.1e-3 9.2e-3 9.2e-3 9.2e-3 9.2e-3   1667    1.0\n",
      "u_predict[48,1]  9.3e-3  2.9e-7 1.2e-5 9.3e-3 9.3e-3 9.3e-3 9.3e-3 9.4e-3   1662    1.0\n",
      "u_predict[49,1]  9.5e-3  3.1e-7 1.3e-5 9.5e-3 9.5e-3 9.5e-3 9.5e-3 9.5e-3   1657    1.0\n",
      "u_predict[50,1]  9.6e-3  3.3e-7 1.3e-5 9.6e-3 9.6e-3 9.6e-3 9.6e-3 9.6e-3   1647    1.0\n",
      "u_predict[51,1]  9.7e-3  3.5e-7 1.4e-5 9.7e-3 9.7e-3 9.7e-3 9.7e-3 9.8e-3   1636    1.0\n",
      "u_predict[52,1]  9.8e-3  3.7e-7 1.5e-5 9.8e-3 9.8e-3 9.8e-3 9.8e-3 9.9e-3   1624    1.0\n",
      "u_predict[53,1]  9.9e-3  3.9e-7 1.6e-5 9.9e-3 9.9e-3 9.9e-3 9.9e-310.0e-3   1613    1.0\n",
      "u_predict[54,1]    0.01  4.1e-7 1.6e-510.0e-310.0e-3   0.01   0.01   0.01   1602    1.0\n",
      "u_predict[55,1]    0.01  4.2e-7 1.7e-5   0.01   0.01   0.01   0.01   0.01   1592    1.0\n",
      "u_predict[56,1]    0.01  4.4e-7 1.7e-5   0.01   0.01   0.01   0.01   0.01   1582    1.0\n",
      "u_predict[57,1]    0.01  4.5e-7 1.8e-5   0.01   0.01   0.01   0.01   0.01   1572    1.0\n",
      "u_predict[58,1]    0.01  4.7e-7 1.8e-5   0.01   0.01   0.01   0.01   0.01   1563    1.0\n",
      "u_predict[59,1]    0.01  4.8e-7 1.9e-5   0.01   0.01   0.01   0.01   0.01   1555    1.0\n",
      "u_predict[60,1]    0.01  4.9e-7 1.9e-5   0.01   0.01   0.01   0.01   0.01   1547    1.0\n",
      "u_predict[61,1]    0.01  5.1e-7 2.0e-5   0.01   0.01   0.01   0.01   0.01   1539    1.0\n",
      "u_predict[62,1]    0.01  5.2e-7 2.0e-5   0.01   0.01   0.01   0.01   0.01   1532    1.0\n",
      "u_predict[63,1]    0.01  5.3e-7 2.1e-5   0.01   0.01   0.01   0.01   0.01   1525    1.0\n",
      "u_predict[64,1]    0.01  5.4e-7 2.1e-5   0.01   0.01   0.01   0.01   0.01   1519    1.0\n",
      "u_predict[65,1]    0.01  5.5e-7 2.1e-5   0.01   0.01   0.01   0.01   0.01   1513    1.0\n",
      "u_predict[66,1]    0.01  5.6e-7 2.2e-5   0.01   0.01   0.01   0.01   0.01   1507    1.0\n",
      "u_predict[67,1]    0.01  5.7e-7 2.2e-510.0e-3   0.01   0.01   0.01   0.01   1502    1.0\n",
      "u_predict[68,1] 10.0e-3  5.7e-7 2.2e-5 9.9e-310.0e-310.0e-310.0e-3   0.01   1497    1.0\n",
      "u_predict[69,1]  9.9e-3  5.8e-7 2.2e-5 9.9e-3 9.9e-3 9.9e-3 9.9e-310.0e-3   1492    1.0\n",
      "u_predict[70,1]  9.8e-3  5.9e-7 2.3e-5 9.8e-3 9.8e-3 9.8e-3 9.9e-3 9.9e-3   1487    1.0\n",
      "u_predict[71,1]  9.8e-3  5.9e-7 2.3e-5 9.7e-3 9.8e-3 9.8e-3 9.8e-3 9.8e-3   1483    1.0\n",
      "u_predict[72,1]  9.7e-3  6.0e-7 2.3e-5 9.7e-3 9.7e-3 9.7e-3 9.7e-3 9.7e-3   1479    1.0\n",
      "u_predict[73,1]  9.6e-3  6.0e-7 2.3e-5 9.6e-3 9.6e-3 9.6e-3 9.6e-3 9.7e-3   1475    1.0\n",
      "u_predict[74,1]  9.5e-3  6.1e-7 2.3e-5 9.5e-3 9.5e-3 9.5e-3 9.6e-3 9.6e-3   1472    1.0\n",
      "u_predict[75,1]  9.4e-3  6.1e-7 2.3e-5 9.4e-3 9.4e-3 9.4e-3 9.5e-3 9.5e-3   1468    1.0\n",
      "u_predict[76,1]  9.4e-3  6.1e-7 2.4e-5 9.3e-3 9.3e-3 9.4e-3 9.4e-3 9.4e-3   1465    1.0\n",
      "u_predict[77,1]  9.3e-3  6.2e-7 2.4e-5 9.2e-3 9.2e-3 9.3e-3 9.3e-3 9.3e-3   1462    1.0\n",
      "u_predict[78,1]  9.2e-3  6.2e-7 2.4e-5 9.1e-3 9.1e-3 9.2e-3 9.2e-3 9.2e-3   1459    1.0\n",
      "u_predict[79,1]  9.1e-3  6.2e-7 2.4e-5 9.0e-3 9.0e-3 9.1e-3 9.1e-3 9.1e-3   1456    1.0\n",
      "u_predict[80,1]  9.0e-3  6.2e-7 2.4e-5 8.9e-3 8.9e-3 9.0e-3 9.0e-3 9.0e-3   1454    1.0\n",
      "u_predict[81,1]  8.9e-3  6.3e-7 2.4e-5 8.8e-3 8.8e-3 8.9e-3 8.9e-3 8.9e-3   1451    1.0\n",
      "u_predict[82,1]  8.7e-3  6.3e-7 2.4e-5 8.7e-3 8.7e-3 8.7e-3 8.8e-3 8.8e-3   1449    1.0\n",
      "u_predict[83,1]  8.6e-3  6.3e-7 2.4e-5 8.6e-3 8.6e-3 8.6e-3 8.7e-3 8.7e-3   1447    1.0\n",
      "u_predict[84,1]  8.5e-3  6.3e-7 2.4e-5 8.5e-3 8.5e-3 8.5e-3 8.5e-3 8.6e-3   1445    1.0\n",
      "u_predict[85,1]  8.4e-3  6.3e-7 2.4e-5 8.4e-3 8.4e-3 8.4e-3 8.4e-3 8.5e-3   1443    1.0\n",
      "u_predict[86,1]  8.3e-3  6.3e-7 2.4e-5 8.3e-3 8.3e-3 8.3e-3 8.3e-3 8.4e-3   1441    1.0\n",
      "u_predict[87,1]  8.2e-3  6.3e-7 2.4e-5 8.1e-3 8.2e-3 8.2e-3 8.2e-3 8.2e-3   1439    1.0\n",
      "u_predict[88,1]  8.1e-3  6.3e-7 2.4e-5 8.0e-3 8.1e-3 8.1e-3 8.1e-3 8.1e-3   1438    1.0\n",
      "u_predict[89,1]  8.0e-3  6.3e-7 2.4e-5 7.9e-3 7.9e-3 8.0e-3 8.0e-3 8.0e-3   1436    1.0\n",
      "u_predict[90,1]  7.8e-3  6.3e-7 2.4e-5 7.8e-3 7.8e-3 7.8e-3 7.9e-3 7.9e-3   1435    1.0\n",
      "u_predict[91,1]  7.7e-3  6.2e-7 2.4e-5 7.7e-3 7.7e-3 7.7e-3 7.7e-3 7.8e-3   1434    1.0\n",
      "u_predict[92,1]  7.6e-3  6.2e-7 2.4e-5 7.6e-3 7.6e-3 7.6e-3 7.6e-3 7.7e-3   1432    1.0\n",
      "u_predict[93,1]  7.5e-3  6.2e-7 2.3e-5 7.5e-3 7.5e-3 7.5e-3 7.5e-3 7.5e-3   1431    1.0\n",
      "u_predict[94,1]  7.4e-3  6.2e-7 2.3e-5 7.3e-3 7.4e-3 7.4e-3 7.4e-3 7.4e-3   1430    1.0\n",
      "u_predict[95,1]  7.3e-3  6.2e-7 2.3e-5 7.2e-3 7.3e-3 7.3e-3 7.3e-3 7.3e-3   1429    1.0\n",
      "u_predict[96,1]  7.2e-3  6.1e-7 2.3e-5 7.1e-3 7.1e-3 7.2e-3 7.2e-3 7.2e-3   1428    1.0\n",
      "u_predict[97,1]  7.0e-3  6.1e-7 2.3e-5 7.0e-3 7.0e-3 7.0e-3 7.1e-3 7.1e-3   1428    1.0\n",
      "u_predict[98,1]  6.9e-3  6.1e-7 2.3e-5 6.9e-3 6.9e-3 6.9e-3 6.9e-3 7.0e-3   1427    1.0\n",
      "u_predict[99,1]  6.8e-3  6.1e-7 2.3e-5 6.8e-3 6.8e-3 6.8e-3 6.8e-3 6.9e-3   1426    1.0\n",
      "u_predict[100,1] 6.7e-3  6.0e-7 2.3e-5 6.7e-3 6.7e-3 6.7e-3 6.7e-3 6.7e-3   1426    1.0\n",
      "u_predict[101,1] 6.6e-3  6.0e-7 2.3e-5 6.5e-3 6.6e-3 6.6e-3 6.6e-3 6.6e-3   1425    1.0\n",
      "u_predict[102,1] 6.5e-3  5.9e-7 2.2e-5 6.4e-3 6.5e-3 6.5e-3 6.5e-3 6.5e-3   1424    1.0\n",
      "u_predict[103,1] 6.4e-3  5.9e-7 2.2e-5 6.3e-3 6.3e-3 6.4e-3 6.4e-3 6.4e-3   1424    1.0\n",
      "u_predict[104,1] 6.2e-3  5.9e-7 2.2e-5 6.2e-3 6.2e-3 6.2e-3 6.3e-3 6.3e-3   1424    1.0\n",
      "u_predict[105,1] 6.1e-3  5.8e-7 2.2e-5 6.1e-3 6.1e-3 6.1e-3 6.1e-3 6.2e-3   1423    1.0\n",
      "u_predict[106,1] 6.0e-3  5.8e-7 2.2e-5 6.0e-3 6.0e-3 6.0e-3 6.0e-3 6.1e-3   1423    1.0\n",
      "u_predict[107,1] 5.9e-3  5.8e-7 2.2e-5 5.9e-3 5.9e-3 5.9e-3 5.9e-3 6.0e-3   1423    1.0\n",
      "u_predict[108,1] 5.8e-3  5.7e-7 2.2e-5 5.8e-3 5.8e-3 5.8e-3 5.8e-3 5.9e-3   1422    1.0\n",
      "u_predict[109,1] 5.7e-3  5.7e-7 2.1e-5 5.7e-3 5.7e-3 5.7e-3 5.7e-3 5.7e-3   1422    1.0\n",
      "u_predict[110,1] 5.6e-3  5.6e-7 2.1e-5 5.6e-3 5.6e-3 5.6e-3 5.6e-3 5.6e-3   1422    1.0\n",
      "u_predict[111,1] 5.5e-3  5.6e-7 2.1e-5 5.5e-3 5.5e-3 5.5e-3 5.5e-3 5.5e-3   1422    1.0\n",
      "u_predict[112,1] 5.4e-3  5.5e-7 2.1e-5 5.4e-3 5.4e-3 5.4e-3 5.4e-3 5.4e-3   1422    1.0\n",
      "u_predict[113,1] 5.3e-3  5.5e-7 2.1e-5 5.2e-3 5.3e-3 5.3e-3 5.3e-3 5.3e-3   1422    1.0\n",
      "u_predict[114,1] 5.2e-3  5.4e-7 2.0e-5 5.1e-3 5.2e-3 5.2e-3 5.2e-3 5.2e-3   1422    1.0\n",
      "u_predict[115,1] 5.1e-3  5.4e-7 2.0e-5 5.0e-3 5.1e-3 5.1e-3 5.1e-3 5.1e-3   1422    1.0\n",
      "u_predict[116,1] 5.0e-3  5.3e-7 2.0e-5 5.0e-3 5.0e-3 5.0e-3 5.0e-3 5.0e-3   1422    1.0\n",
      "u_predict[117,1] 4.9e-3  5.3e-7 2.0e-5 4.9e-3 4.9e-3 4.9e-3 4.9e-3 4.9e-3   1422    1.0\n",
      "u_predict[118,1] 4.8e-3  5.2e-7 2.0e-5 4.8e-3 4.8e-3 4.8e-3 4.8e-3 4.8e-3   1422    1.0\n",
      "u_predict[119,1] 4.7e-3  5.2e-7 2.0e-5 4.7e-3 4.7e-3 4.7e-3 4.7e-3 4.7e-3   1422    1.0\n",
      "u_predict[120,1] 4.6e-3  5.1e-7 1.9e-5 4.6e-3 4.6e-3 4.6e-3 4.6e-3 4.6e-3   1422    1.0\n",
      "u_predict[121,1] 4.5e-3  5.1e-7 1.9e-5 4.5e-3 4.5e-3 4.5e-3 4.5e-3 4.6e-3   1422    1.0\n",
      "u_predict[122,1] 4.4e-3  5.0e-7 1.9e-5 4.4e-3 4.4e-3 4.4e-3 4.4e-3 4.5e-3   1422    1.0\n",
      "u_predict[123,1] 4.3e-3  5.0e-7 1.9e-5 4.3e-3 4.3e-3 4.3e-3 4.4e-3 4.4e-3   1422    1.0\n",
      "u_predict[124,1] 4.3e-3  4.9e-7 1.9e-5 4.2e-3 4.2e-3 4.2e-3 4.3e-3 4.3e-3   1422    1.0\n",
      "u_predict[125,1] 4.2e-3  4.9e-7 1.8e-5 4.1e-3 4.2e-3 4.2e-3 4.2e-3 4.2e-3   1423    1.0\n",
      "u_predict[126,1] 4.1e-3  4.8e-7 1.8e-5 4.0e-3 4.1e-3 4.1e-3 4.1e-3 4.1e-3   1423    1.0\n",
      "u_predict[127,1] 4.0e-3  4.8e-7 1.8e-5 4.0e-3 4.0e-3 4.0e-3 4.0e-3 4.0e-3   1423    1.0\n",
      "u_predict[128,1] 3.9e-3  4.7e-7 1.8e-5 3.9e-3 3.9e-3 3.9e-3 3.9e-3 3.9e-3   1423    1.0\n",
      "u_predict[129,1] 3.8e-3  4.6e-7 1.8e-5 3.8e-3 3.8e-3 3.8e-3 3.8e-3 3.9e-3   1423    1.0\n",
      "u_predict[130,1] 3.8e-3  4.6e-7 1.7e-5 3.7e-3 3.7e-3 3.7e-3 3.8e-3 3.8e-3   1424    1.0\n",
      "u_predict[131,1] 3.7e-3  4.5e-7 1.7e-5 3.6e-3 3.7e-3 3.7e-3 3.7e-3 3.7e-3   1424    1.0\n",
      "u_predict[132,1] 3.6e-3  4.5e-7 1.7e-5 3.6e-3 3.6e-3 3.6e-3 3.6e-3 3.6e-3   1424    1.0\n",
      "u_predict[133,1] 3.5e-3  4.4e-7 1.7e-5 3.5e-3 3.5e-3 3.5e-3 3.5e-3 3.6e-3   1424    1.0\n",
      "u_predict[134,1] 3.4e-3  4.4e-7 1.7e-5 3.4e-3 3.4e-3 3.4e-3 3.5e-3 3.5e-3   1425    1.0\n",
      "u_predict[135,1] 3.4e-3  4.3e-7 1.6e-5 3.3e-3 3.4e-3 3.4e-3 3.4e-3 3.4e-3   1425    1.0\n",
      "u_predict[136,1] 3.3e-3  4.3e-7 1.6e-5 3.3e-3 3.3e-3 3.3e-3 3.3e-3 3.3e-3   1425    1.0\n",
      "u_predict[137,1] 3.2e-3  4.2e-7 1.6e-5 3.2e-3 3.2e-3 3.2e-3 3.2e-3 3.3e-3   1426    1.0\n",
      "u_predict[138,1] 3.2e-3  4.2e-7 1.6e-5 3.1e-3 3.1e-3 3.2e-3 3.2e-3 3.2e-3   1426    1.0\n",
      "u_predict[139,1] 3.1e-3  4.1e-7 1.5e-5 3.1e-3 3.1e-3 3.1e-3 3.1e-3 3.1e-3   1426    1.0\n",
      "u_predict[140,1] 3.0e-3  4.0e-7 1.5e-5 3.0e-3 3.0e-3 3.0e-3 3.0e-3 3.1e-3   1427    1.0\n",
      "u_predict[141,1] 3.0e-3  4.0e-7 1.5e-5 2.9e-3 2.9e-3 3.0e-3 3.0e-3 3.0e-3   1427    1.0\n",
      "u_predict[142,1] 2.9e-3  3.9e-7 1.5e-5 2.9e-3 2.9e-3 2.9e-3 2.9e-3 2.9e-3   1427    1.0\n",
      "u_predict[143,1] 2.8e-3  3.9e-7 1.5e-5 2.8e-3 2.8e-3 2.8e-3 2.8e-3 2.9e-3   1428    1.0\n",
      "u_predict[144,1] 2.8e-3  3.8e-7 1.4e-5 2.7e-3 2.8e-3 2.8e-3 2.8e-3 2.8e-3   1428    1.0\n",
      "u_predict[145,1] 2.7e-3  3.8e-7 1.4e-5 2.7e-3 2.7e-3 2.7e-3 2.7e-3 2.7e-3   1428    1.0\n",
      "u_predict[146,1] 2.6e-3  3.7e-7 1.4e-5 2.6e-3 2.6e-3 2.6e-3 2.7e-3 2.7e-3   1429    1.0\n",
      "u_predict[147,1] 2.6e-3  3.7e-7 1.4e-5 2.6e-3 2.6e-3 2.6e-3 2.6e-3 2.6e-3   1429    1.0\n",
      "u_predict[148,1] 2.5e-3  3.6e-7 1.4e-5 2.5e-3 2.5e-3 2.5e-3 2.5e-3 2.6e-3   1429    1.0\n",
      "u_predict[149,1] 2.5e-3  3.6e-7 1.4e-5 2.4e-3 2.5e-3 2.5e-3 2.5e-3 2.5e-3   1430    1.0\n",
      "u_predict[150,1] 2.4e-3  3.5e-7 1.3e-5 2.4e-3 2.4e-3 2.4e-3 2.4e-3 2.4e-3   1430    1.0\n",
      "u_predict[151,1] 2.4e-3  3.5e-7 1.3e-5 2.3e-3 2.4e-3 2.4e-3 2.4e-3 2.4e-3   1430    1.0\n",
      "u_predict[152,1] 2.3e-3  3.4e-7 1.3e-5 2.3e-3 2.3e-3 2.3e-3 2.3e-3 2.3e-3   1431    1.0\n",
      "u_predict[153,1] 2.3e-3  3.4e-7 1.3e-5 2.2e-3 2.2e-3 2.3e-3 2.3e-3 2.3e-3   1431    1.0\n",
      "u_predict[154,1] 2.2e-3  3.3e-7 1.3e-5 2.2e-3 2.2e-3 2.2e-3 2.2e-3 2.2e-3   1431    1.0\n",
      "u_predict[155,1] 2.2e-3  3.3e-7 1.2e-5 2.1e-3 2.1e-3 2.2e-3 2.2e-3 2.2e-3   1431    1.0\n",
      "u_predict[156,1] 2.1e-3  3.2e-7 1.2e-5 2.1e-3 2.1e-3 2.1e-3 2.1e-3 2.1e-3   1431    1.0\n",
      "u_predict[157,1] 2.1e-3  3.2e-7 1.2e-5 2.0e-3 2.0e-3 2.1e-3 2.1e-3 2.1e-3   1431    1.0\n",
      "u_predict[158,1] 2.0e-3  3.1e-7 1.2e-5 2.0e-3 2.0e-3 2.0e-3 2.0e-3 2.0e-3   1430    1.0\n",
      "u_predict[159,1] 2.0e-3  3.1e-7 1.2e-5 1.9e-3 2.0e-3 2.0e-3 2.0e-3 2.0e-3   1430    1.0\n",
      "u_predict[160,1] 1.9e-3  3.0e-7 1.1e-5 1.9e-3 1.9e-3 1.9e-3 1.9e-3 1.9e-3   1430    1.0\n",
      "u_predict[161,1] 1.9e-3  3.0e-7 1.1e-5 1.9e-3 1.9e-3 1.9e-3 1.9e-3 1.9e-3   1429    1.0\n",
      "u_predict[162,1] 1.8e-3  2.9e-7 1.1e-5 1.8e-3 1.8e-3 1.8e-3 1.8e-3 1.9e-3   1429    1.0\n",
      "u_predict[163,1] 1.8e-3  2.9e-7 1.1e-5 1.8e-3 1.8e-3 1.8e-3 1.8e-3 1.8e-3   1429    1.0\n",
      "u_predict[164,1] 1.7e-3  2.8e-7 1.1e-5 1.7e-3 1.7e-3 1.7e-3 1.8e-3 1.8e-3   1429    1.0\n",
      "u_predict[165,1] 1.7e-3  2.8e-7 1.1e-5 1.7e-3 1.7e-3 1.7e-3 1.7e-3 1.7e-3   1428    1.0\n",
      "u_predict[166,1] 1.7e-3  2.8e-7 1.0e-5 1.6e-3 1.7e-3 1.7e-3 1.7e-3 1.7e-3   1428    1.0\n",
      "u_predict[167,1] 1.6e-3  2.7e-7 1.0e-5 1.6e-3 1.6e-3 1.6e-3 1.6e-3 1.6e-3   1428    1.0\n",
      "u_predict[168,1] 1.6e-3  2.7e-7 1.0e-5 1.6e-3 1.6e-3 1.6e-3 1.6e-3 1.6e-3   1428    1.0\n",
      "u_predict[169,1] 1.5e-3  2.6e-7 9.9e-6 1.5e-3 1.5e-3 1.5e-3 1.6e-3 1.6e-3   1427    1.0\n",
      "u_predict[170,1] 1.5e-3  2.6e-7 9.7e-6 1.5e-3 1.5e-3 1.5e-3 1.5e-3 1.5e-3   1427    1.0\n",
      "u_predict[171,1] 1.5e-3  2.5e-7 9.6e-6 1.5e-3 1.5e-3 1.5e-3 1.5e-3 1.5e-3   1427    1.0\n",
      "u_predict[172,1] 1.4e-3  2.5e-7 9.4e-6 1.4e-3 1.4e-3 1.4e-3 1.4e-3 1.5e-3   1427    1.0\n",
      "u_predict[173,1] 1.4e-3  2.5e-7 9.3e-6 1.4e-3 1.4e-3 1.4e-3 1.4e-3 1.4e-3   1427    1.0\n",
      "u_predict[174,1] 1.4e-3  2.4e-7 9.1e-6 1.4e-3 1.4e-3 1.4e-3 1.4e-3 1.4e-3   1426    1.0\n",
      "u_predict[175,1] 1.3e-3  2.4e-7 9.0e-6 1.3e-3 1.3e-3 1.3e-3 1.3e-3 1.4e-3   1426    1.0\n",
      "u_predict[176,1] 1.3e-3  2.3e-7 8.8e-6 1.3e-3 1.3e-3 1.3e-3 1.3e-3 1.3e-3   1426    1.0\n",
      "u_predict[177,1] 1.3e-3  2.3e-7 8.7e-6 1.3e-3 1.3e-3 1.3e-3 1.3e-3 1.3e-3   1426    1.0\n",
      "u_predict[178,1] 1.2e-3  2.3e-7 8.5e-6 1.2e-3 1.2e-3 1.2e-3 1.3e-3 1.3e-3   1426    1.0\n",
      "u_predict[179,1] 1.2e-3  2.2e-7 8.4e-6 1.2e-3 1.2e-3 1.2e-3 1.2e-3 1.2e-3   1425    1.0\n",
      "u_predict[180,1] 1.2e-3  2.2e-7 8.2e-6 1.2e-3 1.2e-3 1.2e-3 1.2e-3 1.2e-3   1425    1.0\n",
      "u_predict[181,1] 1.2e-3  2.1e-7 8.1e-6 1.1e-3 1.2e-3 1.2e-3 1.2e-3 1.2e-3   1425    1.0\n",
      "u_predict[182,1] 1.1e-3  2.1e-7 7.9e-6 1.1e-3 1.1e-3 1.1e-3 1.1e-3 1.1e-3   1425    1.0\n",
      "u_predict[183,1] 1.1e-3  2.1e-7 7.8e-6 1.1e-3 1.1e-3 1.1e-3 1.1e-3 1.1e-3   1425    1.0\n",
      "u_predict[184,1] 1.1e-3  2.0e-7 7.7e-6 1.1e-3 1.1e-3 1.1e-3 1.1e-3 1.1e-3   1425    1.0\n",
      "u_predict[185,1] 1.1e-3  2.0e-7 7.5e-6 1.0e-3 1.0e-3 1.0e-3 1.1e-3 1.1e-3   1424    1.0\n",
      "u_predict[186,1] 1.0e-3  2.0e-7 7.4e-6 1.0e-3 1.0e-3 1.0e-3 1.0e-3 1.0e-3   1424    1.0\n",
      "u_predict[187,1]10.0e-4  1.9e-7 7.3e-6 9.9e-4 9.9e-410.0e-4 1.0e-3 1.0e-3   1424    1.0\n",
      "u_predict[188,1] 9.8e-4  1.9e-7 7.1e-6 9.6e-4 9.7e-4 9.7e-4 9.8e-4 9.9e-4   1424    1.0\n",
      "u_predict[189,1] 9.5e-4  1.9e-7 7.0e-6 9.4e-4 9.5e-4 9.5e-4 9.6e-4 9.7e-4   1424    1.0\n",
      "u_predict[190,1] 9.3e-4  1.8e-7 6.9e-6 9.2e-4 9.2e-4 9.3e-4 9.3e-4 9.4e-4   1424    1.0\n",
      "u_predict[191,1] 9.1e-4  1.8e-7 6.7e-6 8.9e-4 9.0e-4 9.1e-4 9.1e-4 9.2e-4   1424    1.0\n",
      "u_predict[192,1] 8.8e-4  1.8e-7 6.6e-6 8.7e-4 8.8e-4 8.8e-4 8.9e-4 9.0e-4   1423    1.0\n",
      "u_predict[193,1] 8.6e-4  1.7e-7 6.5e-6 8.5e-4 8.6e-4 8.6e-4 8.7e-4 8.7e-4   1423    1.0\n",
      "u_predict[194,1] 8.4e-4  1.7e-7 6.4e-6 8.3e-4 8.4e-4 8.4e-4 8.4e-4 8.5e-4   1423    1.0\n",
      "u_predict[195,1] 8.2e-4  1.7e-7 6.3e-6 8.1e-4 8.1e-4 8.2e-4 8.2e-4 8.3e-4   1423    1.0\n",
      "u_predict[196,1] 8.0e-4  1.6e-7 6.1e-6 7.9e-4 7.9e-4 8.0e-4 8.0e-4 8.1e-4   1423    1.0\n",
      "u_predict[197,1] 7.8e-4  1.6e-7 6.0e-6 7.7e-4 7.7e-4 7.8e-4 7.8e-4 7.9e-4   1423    1.0\n",
      "u_predict[198,1] 7.6e-4  1.6e-7 5.9e-6 7.5e-4 7.6e-4 7.6e-4 7.6e-4 7.7e-4   1423    1.0\n",
      "u_predict[199,1] 7.4e-4  1.5e-7 5.8e-6 7.3e-4 7.4e-4 7.4e-4 7.4e-4 7.5e-4   1423    1.0\n",
      "u_predict[200,1] 7.2e-4  1.5e-7 5.7e-6 7.1e-4 7.2e-4 7.2e-4 7.3e-4 7.3e-4   1422    1.0\n",
      "u_predict[201,1] 7.0e-4  1.5e-7 5.6e-6 6.9e-4 7.0e-4 7.0e-4 7.1e-4 7.2e-4   1422    1.0\n",
      "u_predict[202,1] 6.9e-4  1.5e-7 5.5e-6 6.8e-4 6.8e-4 6.9e-4 6.9e-4 7.0e-4   1422    1.0\n",
      "u_predict[203,1] 6.7e-4  1.4e-7 5.4e-6 6.6e-4 6.7e-4 6.7e-4 6.7e-4 6.8e-4   1422    1.0\n",
      "u_predict[204,1] 6.5e-4  1.4e-7 5.3e-6 6.4e-4 6.5e-4 6.5e-4 6.6e-4 6.6e-4   1422    1.0\n",
      "u_predict[205,1] 6.4e-4  1.4e-7 5.2e-6 6.3e-4 6.3e-4 6.4e-4 6.4e-4 6.5e-4   1422    1.0\n",
      "u_predict[206,1] 6.2e-4  1.3e-7 5.1e-6 6.1e-4 6.2e-4 6.2e-4 6.2e-4 6.3e-4   1422    1.0\n",
      "u_predict[207,1] 6.1e-4  1.3e-7 5.0e-6 6.0e-4 6.0e-4 6.1e-4 6.1e-4 6.2e-4   1422    1.0\n",
      "u_predict[208,1] 5.9e-4  1.3e-7 4.9e-6 5.8e-4 5.9e-4 5.9e-4 5.9e-4 6.0e-4   1422    1.0\n",
      "u_predict[209,1] 5.8e-4  1.3e-7 4.8e-6 5.7e-4 5.7e-4 5.8e-4 5.8e-4 5.8e-4   1422    1.0\n",
      "u_predict[210,1] 5.6e-4  1.2e-7 4.7e-6 5.5e-4 5.6e-4 5.6e-4 5.6e-4 5.7e-4   1422    1.0\n",
      "u_predict[211,1] 5.5e-4  1.2e-7 4.6e-6 5.4e-4 5.4e-4 5.5e-4 5.5e-4 5.6e-4   1421    1.0\n",
      "u_predict[212,1] 5.3e-4  1.2e-7 4.5e-6 5.2e-4 5.3e-4 5.3e-4 5.4e-4 5.4e-4   1421    1.0\n",
      "u_predict[213,1] 5.2e-4  1.2e-7 4.4e-6 5.1e-4 5.2e-4 5.2e-4 5.2e-4 5.3e-4   1421    1.0\n",
      "u_predict[214,1] 5.1e-4  1.2e-7 4.3e-6 5.0e-4 5.0e-4 5.1e-4 5.1e-4 5.1e-4   1421    1.0\n",
      "u_predict[215,1] 4.9e-4  1.1e-7 4.3e-6 4.9e-4 4.9e-4 4.9e-4 5.0e-4 5.0e-4   1421    1.0\n",
      "u_predict[216,1] 4.8e-4  1.1e-7 4.2e-6 4.7e-4 4.8e-4 4.8e-4 4.8e-4 4.9e-4   1421    1.0\n",
      "u_predict[217,1] 4.7e-4  1.1e-7 4.1e-6 4.6e-4 4.7e-4 4.7e-4 4.7e-4 4.8e-4   1421    1.0\n",
      "u_predict[218,1] 4.6e-4  1.1e-7 4.0e-6 4.5e-4 4.5e-4 4.6e-4 4.6e-4 4.6e-4   1421    1.0\n",
      "u_predict[219,1] 4.5e-4  1.0e-7 3.9e-6 4.4e-4 4.4e-4 4.4e-4 4.5e-4 4.5e-4   1421    1.0\n",
      "u_predict[220,1] 4.3e-4  1.0e-7 3.9e-6 4.3e-4 4.3e-4 4.3e-4 4.4e-4 4.4e-4   1421    1.0\n",
      "u_predict[221,1] 4.2e-4  1.0e-7 3.8e-6 4.2e-4 4.2e-4 4.2e-4 4.3e-4 4.3e-4   1421    1.0\n",
      "u_predict[222,1] 4.1e-4  9.8e-8 3.7e-6 4.1e-4 4.1e-4 4.1e-4 4.1e-4 4.2e-4   1421    1.0\n",
      "u_predict[223,1] 4.0e-4  9.6e-8 3.6e-6 3.9e-4 4.0e-4 4.0e-4 4.0e-4 4.1e-4   1421    1.0\n",
      "u_predict[224,1] 3.9e-4  9.4e-8 3.6e-6 3.8e-4 3.9e-4 3.9e-4 3.9e-4 4.0e-4   1421    1.0\n",
      "u_predict[225,1] 3.8e-4  9.2e-8 3.5e-6 3.7e-4 3.8e-4 3.8e-4 3.8e-4 3.9e-4   1420    1.0\n",
      "u_predict[226,1] 3.7e-4  9.0e-8 3.4e-6 3.7e-4 3.7e-4 3.7e-4 3.7e-4 3.8e-4   1420    1.0\n",
      "u_predict[227,1] 3.6e-4  8.9e-8 3.3e-6 3.6e-4 3.6e-4 3.6e-4 3.6e-4 3.7e-4   1420    1.0\n",
      "u_predict[228,1] 3.5e-4  8.7e-8 3.3e-6 3.5e-4 3.5e-4 3.5e-4 3.5e-4 3.6e-4   1420    1.0\n",
      "u_predict[229,1] 3.4e-4  8.5e-8 3.2e-6 3.4e-4 3.4e-4 3.4e-4 3.5e-4 3.5e-4   1420    1.0\n",
      "u_predict[230,1] 3.3e-4  8.3e-8 3.1e-6 3.3e-4 3.3e-4 3.3e-4 3.4e-4 3.4e-4   1420    1.0\n",
      "u_predict[231,1] 3.3e-4  8.2e-8 3.1e-6 3.2e-4 3.2e-4 3.3e-4 3.3e-4 3.3e-4   1420    1.0\n",
      "u_predict[232,1] 3.2e-4  8.0e-8 3.0e-6 3.1e-4 3.2e-4 3.2e-4 3.2e-4 3.2e-4   1420    1.0\n",
      "u_predict[233,1] 3.1e-4  7.8e-8 2.9e-6 3.0e-4 3.1e-4 3.1e-4 3.1e-4 3.2e-4   1420    1.0\n",
      "u_predict[234,1] 3.0e-4  7.7e-8 2.9e-6 3.0e-4 3.0e-4 3.0e-4 3.0e-4 3.1e-4   1420    1.0\n",
      "u_predict[235,1] 2.9e-4  7.5e-8 2.8e-6 2.9e-4 2.9e-4 2.9e-4 3.0e-4 3.0e-4   1420    1.0\n",
      "u_predict[236,1] 2.9e-4  7.4e-8 2.8e-6 2.8e-4 2.8e-4 2.9e-4 2.9e-4 2.9e-4   1420    1.0\n",
      "u_predict[237,1] 2.8e-4  7.2e-8 2.7e-6 2.7e-4 2.8e-4 2.8e-4 2.8e-4 2.8e-4   1420    1.0\n",
      "u_predict[238,1] 2.7e-4  7.0e-8 2.7e-6 2.7e-4 2.7e-4 2.7e-4 2.7e-4 2.8e-4   1420    1.0\n",
      "u_predict[239,1] 2.6e-4  6.9e-8 2.6e-6 2.6e-4 2.6e-4 2.6e-4 2.7e-4 2.7e-4   1420    1.0\n",
      "u_predict[240,1] 2.6e-4  6.8e-8 2.5e-6 2.5e-4 2.6e-4 2.6e-4 2.6e-4 2.6e-4   1420    1.0\n",
      "u_predict[241,1] 2.5e-4  6.6e-8 2.5e-6 2.5e-4 2.5e-4 2.5e-4 2.5e-4 2.6e-4   1420    1.0\n",
      "u_predict[242,1] 2.4e-4  6.5e-8 2.4e-6 2.4e-4 2.4e-4 2.4e-4 2.5e-4 2.5e-4   1420    1.0\n",
      "u_predict[243,1] 2.4e-4  6.3e-8 2.4e-6 2.3e-4 2.4e-4 2.4e-4 2.4e-4 2.4e-4   1420    1.0\n",
      "u_predict[244,1] 2.3e-4  6.2e-8 2.3e-6 2.3e-4 2.3e-4 2.3e-4 2.3e-4 2.4e-4   1420    1.0\n",
      "u_predict[245,1] 2.3e-4  6.1e-8 2.3e-6 2.2e-4 2.2e-4 2.3e-4 2.3e-4 2.3e-4   1420    1.0\n",
      "u_predict[246,1] 2.2e-4  5.9e-8 2.2e-6 2.2e-4 2.2e-4 2.2e-4 2.2e-4 2.2e-4   1419    1.0\n",
      "u_predict[247,1] 2.1e-4  5.8e-8 2.2e-6 2.1e-4 2.1e-4 2.1e-4 2.2e-4 2.2e-4   1419    1.0\n",
      "u_predict[248,1] 2.1e-4  5.7e-8 2.1e-6 2.0e-4 2.1e-4 2.1e-4 2.1e-4 2.1e-4   1419    1.0\n",
      "u_predict[249,1] 2.0e-4  5.6e-8 2.1e-6 2.0e-4 2.0e-4 2.0e-4 2.0e-4 2.1e-4   1419    1.0\n",
      "u_predict[250,1] 2.0e-4  5.5e-8 2.1e-6 1.9e-4 2.0e-4 2.0e-4 2.0e-4 2.0e-4   1419    1.0\n",
      "u_predict[251,1] 1.9e-4  5.3e-8 2.0e-6 1.9e-4 1.9e-4 1.9e-4 1.9e-4 2.0e-4   1419    1.0\n",
      "u_predict[252,1] 1.9e-4  5.2e-8 2.0e-6 1.8e-4 1.9e-4 1.9e-4 1.9e-4 1.9e-4   1419    1.0\n",
      "u_predict[253,1] 1.8e-4  5.1e-8 1.9e-6 1.8e-4 1.8e-4 1.8e-4 1.8e-4 1.9e-4   1419    1.0\n",
      "u_predict[254,1] 1.8e-4  5.0e-8 1.9e-6 1.7e-4 1.8e-4 1.8e-4 1.8e-4 1.8e-4   1419    1.0\n",
      "u_predict[255,1] 1.7e-4  4.9e-8 1.8e-6 1.7e-4 1.7e-4 1.7e-4 1.7e-4 1.8e-4   1419    1.0\n",
      "u_predict[256,1] 1.7e-4  4.8e-8 1.8e-6 1.7e-4 1.7e-4 1.7e-4 1.7e-4 1.7e-4   1419    1.0\n",
      "u_predict[257,1] 1.6e-4  4.7e-8 1.8e-6 1.6e-4 1.6e-4 1.6e-4 1.7e-4 1.7e-4   1419    1.0\n",
      "u_predict[258,1] 1.6e-4  4.6e-8 1.7e-6 1.6e-4 1.6e-4 1.6e-4 1.6e-4 1.6e-4   1419    1.0\n",
      "u_predict[259,1] 1.6e-4  4.5e-8 1.7e-6 1.5e-4 1.5e-4 1.6e-4 1.6e-4 1.6e-4   1419    1.0\n",
      "u_predict[260,1] 1.5e-4  4.4e-8 1.7e-6 1.5e-4 1.5e-4 1.5e-4 1.5e-4 1.5e-4   1419    1.0\n",
      "u_predict[261,1] 1.5e-4  4.3e-8 1.6e-6 1.4e-4 1.5e-4 1.5e-4 1.5e-4 1.5e-4   1419    1.0\n",
      "u_predict[262,1] 1.4e-4  4.2e-8 1.6e-6 1.4e-4 1.4e-4 1.4e-4 1.4e-4 1.5e-4   1419    1.0\n",
      "u_predict[263,1] 1.4e-4  4.1e-8 1.5e-6 1.4e-4 1.4e-4 1.4e-4 1.4e-4 1.4e-4   1419    1.0\n",
      "u_predict[264,1] 1.4e-4  4.0e-8 1.5e-6 1.3e-4 1.4e-4 1.4e-4 1.4e-4 1.4e-4   1419    1.0\n",
      "u_predict[265,1] 1.3e-4  3.9e-8 1.5e-6 1.3e-4 1.3e-4 1.3e-4 1.3e-4 1.4e-4   1419    1.0\n",
      "u_predict[266,1] 1.3e-4  3.8e-8 1.4e-6 1.3e-4 1.3e-4 1.3e-4 1.3e-4 1.3e-4   1419    1.0\n",
      "u_predict[267,1] 1.3e-4  3.8e-8 1.4e-6 1.2e-4 1.2e-4 1.3e-4 1.3e-4 1.3e-4   1419    1.0\n",
      "u_predict[268,1] 1.2e-4  3.7e-8 1.4e-6 1.2e-4 1.2e-4 1.2e-4 1.2e-4 1.3e-4   1419    1.0\n",
      "u_predict[269,1] 1.2e-4  3.6e-8 1.4e-6 1.2e-4 1.2e-4 1.2e-4 1.2e-4 1.2e-4   1419    1.0\n",
      "u_predict[270,1] 1.2e-4  3.5e-8 1.3e-6 1.1e-4 1.1e-4 1.2e-4 1.2e-4 1.2e-4   1419    1.0\n",
      "u_predict[271,1] 1.1e-4  3.4e-8 1.3e-6 1.1e-4 1.1e-4 1.1e-4 1.1e-4 1.2e-4   1419    1.0\n",
      "u_predict[272,1] 1.1e-4  3.4e-8 1.3e-6 1.1e-4 1.1e-4 1.1e-4 1.1e-4 1.1e-4   1419    1.0\n",
      "u_predict[273,1] 1.1e-4  3.3e-8 1.2e-6 1.0e-4 1.1e-4 1.1e-4 1.1e-4 1.1e-4   1419    1.0\n",
      "u_predict[274,1] 1.0e-4  3.2e-8 1.2e-6 1.0e-4 1.0e-4 1.0e-4 1.0e-4 1.1e-4   1419    1.0\n",
      "u_predict[275,1] 1.0e-4  3.1e-8 1.2e-6 9.9e-5 1.0e-4 1.0e-4 1.0e-4 1.0e-4   1419    1.0\n",
      "u_predict[276,1] 9.9e-5  3.1e-8 1.2e-6 9.6e-5 9.8e-5 9.9e-5 9.9e-5 1.0e-4   1419    1.0\n",
      "u_predict[277,1] 9.6e-5  3.0e-8 1.1e-6 9.4e-5 9.5e-5 9.6e-5 9.7e-5 9.8e-5   1419    1.0\n",
      "u_predict[278,1] 9.3e-5  2.9e-8 1.1e-6 9.1e-5 9.3e-5 9.3e-5 9.4e-5 9.6e-5   1419    1.0\n",
      "u_predict[279,1] 9.1e-5  2.9e-8 1.1e-6 8.9e-5 9.0e-5 9.1e-5 9.2e-5 9.3e-5   1419    1.0\n",
      "u_predict[280,1] 8.8e-5  2.8e-8 1.1e-6 8.7e-5 8.8e-5 8.8e-5 8.9e-5 9.1e-5   1419    1.0\n",
      "u_predict[281,1] 8.6e-5  2.7e-8 1.0e-6 8.4e-5 8.5e-5 8.6e-5 8.7e-5 8.8e-5   1419    1.0\n",
      "u_predict[282,1] 8.4e-5  2.7e-8 1.0e-6 8.2e-5 8.3e-5 8.4e-5 8.4e-5 8.6e-5   1419    1.0\n",
      "u_predict[283,1] 8.2e-5  2.6e-8 9.9e-7 8.0e-5 8.1e-5 8.2e-5 8.2e-5 8.4e-5   1419    1.0\n",
      "u_predict[284,1] 7.9e-5  2.6e-8 9.6e-7 7.8e-5 7.9e-5 7.9e-5 8.0e-5 8.1e-5   1418    1.0\n",
      "u_predict[285,1] 7.7e-5  2.5e-8 9.4e-7 7.6e-5 7.7e-5 7.7e-5 7.8e-5 7.9e-5   1418    1.0\n",
      "u_predict[286,1] 7.5e-5  2.4e-8 9.2e-7 7.3e-5 7.5e-5 7.5e-5 7.6e-5 7.7e-5   1418    1.0\n",
      "u_predict[287,1] 7.3e-5  2.4e-8 9.0e-7 7.2e-5 7.3e-5 7.3e-5 7.4e-5 7.5e-5   1418    1.0\n",
      "u_predict[288,1] 7.1e-5  2.3e-8 8.8e-7 7.0e-5 7.1e-5 7.1e-5 7.2e-5 7.3e-5   1418    1.0\n",
      "u_predict[289,1] 6.9e-5  2.3e-8 8.6e-7 6.8e-5 6.9e-5 6.9e-5 7.0e-5 7.1e-5   1418    1.0\n",
      "u_predict[290,1] 6.7e-5  2.2e-8 8.4e-7 6.6e-5 6.7e-5 6.7e-5 6.8e-5 6.9e-5   1418    1.0\n",
      "u_predict[291,1] 6.6e-5  2.2e-8 8.2e-7 6.4e-5 6.5e-5 6.6e-5 6.6e-5 6.7e-5   1418    1.0\n",
      "u_predict[292,1] 6.4e-5  2.1e-8 8.0e-7 6.2e-5 6.3e-5 6.4e-5 6.4e-5 6.5e-5   1418    1.0\n",
      "u_predict[293,1] 6.2e-5  2.1e-8 7.8e-7 6.1e-5 6.2e-5 6.2e-5 6.3e-5 6.4e-5   1418    1.0\n",
      "u_predict[294,1] 6.0e-5  2.0e-8 7.6e-7 5.9e-5 6.0e-5 6.0e-5 6.1e-5 6.2e-5   1418    1.0\n",
      "u_predict[295,1] 5.9e-5  2.0e-8 7.5e-7 5.7e-5 5.8e-5 5.9e-5 5.9e-5 6.0e-5   1418    1.0\n",
      "u_predict[296,1] 5.7e-5  1.9e-8 7.3e-7 5.6e-5 5.7e-5 5.7e-5 5.8e-5 5.9e-5   1418    1.0\n",
      "u_predict[297,1] 5.6e-5  1.9e-8 7.1e-7 5.4e-5 5.5e-5 5.6e-5 5.6e-5 5.7e-5   1418    1.0\n",
      "u_predict[298,1] 5.4e-5  1.8e-8 7.0e-7 5.3e-5 5.4e-5 5.4e-5 5.5e-5 5.6e-5   1418    1.0\n",
      "u_predict[299,1] 5.3e-5  1.8e-8 6.8e-7 5.1e-5 5.2e-5 5.3e-5 5.3e-5 5.4e-5   1418    1.0\n",
      "u_predict[300,1] 5.1e-5  1.8e-8 6.6e-7 5.0e-5 5.1e-5 5.1e-5 5.2e-5 5.3e-5   1418    1.0\n",
      "u_predict[301,1] 5.0e-5  1.7e-8 6.5e-7 4.9e-5 5.0e-5 5.0e-5 5.0e-5 5.1e-5   1418    1.0\n",
      "u_predict[302,1] 4.9e-5  1.7e-8 6.3e-7 4.7e-5 4.8e-5 4.9e-5 4.9e-5 5.0e-5   1418    1.0\n",
      "u_predict[303,1] 4.7e-5  1.6e-8 6.2e-7 4.6e-5 4.7e-5 4.7e-5 4.8e-5 4.9e-5   1418    1.0\n",
      "u_predict[304,1] 4.6e-5  1.6e-8 6.1e-7 4.5e-5 4.6e-5 4.6e-5 4.6e-5 4.7e-5   1418    1.0\n",
      "u_predict[305,1] 4.5e-5  1.6e-8 5.9e-7 4.4e-5 4.4e-5 4.5e-5 4.5e-5 4.6e-5   1418    1.0\n",
      "u_predict[306,1] 4.4e-5  1.5e-8 5.8e-7 4.2e-5 4.3e-5 4.4e-5 4.4e-5 4.5e-5   1418    1.0\n",
      "u_predict[307,1] 4.2e-5  1.5e-8 5.6e-7 4.1e-5 4.2e-5 4.2e-5 4.3e-5 4.3e-5   1418    1.0\n",
      "u_predict[308,1] 4.1e-5  1.5e-8 5.5e-7 4.0e-5 4.1e-5 4.1e-5 4.2e-5 4.2e-5   1418    1.0\n",
      "u_predict[309,1] 4.0e-5  1.4e-8 5.4e-7 3.9e-5 4.0e-5 4.0e-5 4.0e-5 4.1e-5   1418    1.0\n",
      "u_predict[310,1] 3.9e-5  1.4e-8 5.3e-7 3.8e-5 3.9e-5 3.9e-5 3.9e-5 4.0e-5   1418    1.0\n",
      "u_predict[311,1] 3.8e-5  1.4e-8 5.1e-7 3.7e-5 3.8e-5 3.8e-5 3.8e-5 3.9e-5   1418    1.0\n",
      "u_predict[312,1] 3.7e-5  1.3e-8 5.0e-7 3.6e-5 3.7e-5 3.7e-5 3.7e-5 3.8e-5   1418    1.0\n",
      "u_predict[313,1] 3.6e-5  1.3e-8 4.9e-7 3.5e-5 3.6e-5 3.6e-5 3.6e-5 3.7e-5   1418    1.0\n",
      "u_predict[314,1] 3.5e-5  1.3e-8 4.8e-7 3.4e-5 3.5e-5 3.5e-5 3.5e-5 3.6e-5   1418    1.0\n",
      "u_predict[315,1] 3.4e-5  1.2e-8 4.7e-7 3.3e-5 3.4e-5 3.4e-5 3.4e-5 3.5e-5   1418    1.0\n",
      "u_predict[316,1] 3.3e-5  1.2e-8 4.6e-7 3.2e-5 3.3e-5 3.3e-5 3.3e-5 3.4e-5   1418    1.0\n",
      "u_predict[317,1] 3.2e-5  1.2e-8 4.5e-7 3.1e-5 3.2e-5 3.2e-5 3.2e-5 3.3e-5   1418    1.0\n",
      "u_predict[318,1] 3.1e-5  1.2e-8 4.3e-7 3.1e-5 3.1e-5 3.1e-5 3.2e-5 3.2e-5   1418    1.0\n",
      "u_predict[319,1] 3.0e-5  1.1e-8 4.2e-7 3.0e-5 3.0e-5 3.0e-5 3.1e-5 3.1e-5   1418    1.0\n",
      "u_predict[320,1] 3.0e-5  1.1e-8 4.1e-7 2.9e-5 2.9e-5 3.0e-5 3.0e-5 3.0e-5   1418    1.0\n",
      "u_predict[321,1] 2.9e-5  1.1e-8 4.0e-7 2.8e-5 2.9e-5 2.9e-5 2.9e-5 3.0e-5   1418    1.0\n",
      "u_predict[322,1] 2.8e-5  1.0e-8 4.0e-7 2.7e-5 2.8e-5 2.8e-5 2.8e-5 2.9e-5   1418    1.0\n",
      "u_predict[323,1] 2.7e-5  1.0e-8 3.9e-7 2.7e-5 2.7e-5 2.7e-5 2.8e-5 2.8e-5   1418    1.0\n",
      "u_predict[324,1] 2.7e-5  1.0e-8 3.8e-7 2.6e-5 2.6e-5 2.7e-5 2.7e-5 2.7e-5   1418    1.0\n",
      "u_predict[325,1] 2.6e-5  9.8e-9 3.7e-7 2.5e-5 2.6e-5 2.6e-5 2.6e-5 2.7e-5   1418    1.0\n",
      "u_predict[326,1] 2.5e-5  9.5e-9 3.6e-7 2.4e-5 2.5e-5 2.5e-5 2.5e-5 2.6e-5   1418    1.0\n",
      "u_predict[327,1] 2.4e-5  9.3e-9 3.5e-7 2.4e-5 2.4e-5 2.4e-5 2.5e-5 2.5e-5   1418    1.0\n",
      "u_predict[328,1] 2.4e-5  9.1e-9 3.4e-7 2.3e-5 2.4e-5 2.4e-5 2.4e-5 2.4e-5   1418    1.0\n",
      "u_predict[329,1] 2.3e-5  8.9e-9 3.3e-7 2.2e-5 2.3e-5 2.3e-5 2.3e-5 2.4e-5   1418    1.0\n",
      "u_predict[330,1] 2.2e-5  8.6e-9 3.3e-7 2.2e-5 2.2e-5 2.2e-5 2.3e-5 2.3e-5   1418    1.0\n",
      "u_predict[331,1] 2.2e-5  8.4e-9 3.2e-7 2.1e-5 2.2e-5 2.2e-5 2.2e-5 2.2e-5   1418    1.0\n",
      "u_predict[332,1] 2.1e-5  8.2e-9 3.1e-7 2.1e-5 2.1e-5 2.1e-5 2.1e-5 2.2e-5   1418    1.0\n",
      "u_predict[333,1] 2.1e-5  8.0e-9 3.0e-7 2.0e-5 2.0e-5 2.1e-5 2.1e-5 2.1e-5   1418    1.0\n",
      "u_predict[334,1] 2.0e-5  7.8e-9 3.0e-7 2.0e-5 2.0e-5 2.0e-5 2.0e-5 2.1e-5   1418    1.0\n",
      "u_predict[335,1] 2.0e-5  7.7e-9 2.9e-7 1.9e-5 1.9e-5 2.0e-5 2.0e-5 2.0e-5   1418    1.0\n",
      "u_predict[336,1] 1.9e-5  7.5e-9 2.8e-7 1.8e-5 1.9e-5 1.9e-5 1.9e-5 2.0e-5   1418    1.0\n",
      "u_predict[337,1] 1.8e-5  7.3e-9 2.7e-7 1.8e-5 1.8e-5 1.8e-5 1.9e-5 1.9e-5   1418    1.0\n",
      "u_predict[338,1] 1.8e-5  7.1e-9 2.7e-7 1.7e-5 1.8e-5 1.8e-5 1.8e-5 1.9e-5   1418    1.0\n",
      "u_predict[339,1] 1.7e-5  6.9e-9 2.6e-7 1.7e-5 1.7e-5 1.7e-5 1.8e-5 1.8e-5   1418    1.0\n",
      "u_predict[340,1] 1.7e-5  6.8e-9 2.6e-7 1.7e-5 1.7e-5 1.7e-5 1.7e-5 1.8e-5   1418    1.0\n",
      "u_predict[341,1] 1.7e-5  6.6e-9 2.5e-7 1.6e-5 1.6e-5 1.7e-5 1.7e-5 1.7e-5   1418    1.0\n",
      "u_predict[342,1] 1.6e-5  6.5e-9 2.4e-7 1.6e-5 1.6e-5 1.6e-5 1.6e-5 1.7e-5   1418    1.0\n",
      "u_predict[343,1] 1.6e-5  6.3e-9 2.4e-7 1.5e-5 1.5e-5 1.6e-5 1.6e-5 1.6e-5   1418    1.0\n",
      "u_predict[344,1] 1.5e-5  6.2e-9 2.3e-7 1.5e-5 1.5e-5 1.5e-5 1.5e-5 1.6e-5   1418    1.0\n",
      "u_predict[345,1] 1.5e-5  6.0e-9 2.3e-7 1.4e-5 1.5e-5 1.5e-5 1.5e-5 1.5e-5   1418    1.0\n",
      "u_predict[346,1] 1.4e-5  5.9e-9 2.2e-7 1.4e-5 1.4e-5 1.4e-5 1.5e-5 1.5e-5   1418    1.0\n",
      "u_predict[347,1] 1.4e-5  5.7e-9 2.2e-7 1.4e-5 1.4e-5 1.4e-5 1.4e-5 1.4e-5   1418    1.0\n",
      "u_predict[348,1] 1.4e-5  5.6e-9 2.1e-7 1.3e-5 1.3e-5 1.4e-5 1.4e-5 1.4e-5   1418    1.0\n",
      "u_predict[349,1] 1.3e-5  5.5e-9 2.1e-7 1.3e-5 1.3e-5 1.3e-5 1.3e-5 1.4e-5   1418    1.0\n",
      "u_predict[350,1] 1.3e-5  5.3e-9 2.0e-7 1.3e-5 1.3e-5 1.3e-5 1.3e-5 1.3e-5   1418    1.0\n",
      "u_predict[351,1] 1.3e-5  5.2e-9 2.0e-7 1.2e-5 1.2e-5 1.3e-5 1.3e-5 1.3e-5   1418    1.0\n",
      "u_predict[352,1] 1.2e-5  5.1e-9 1.9e-7 1.2e-5 1.2e-5 1.2e-5 1.2e-5 1.3e-5   1418    1.0\n",
      "u_predict[353,1] 1.2e-5  4.9e-9 1.9e-7 1.2e-5 1.2e-5 1.2e-5 1.2e-5 1.2e-5   1418    1.0\n",
      "u_predict[354,1] 1.2e-5  4.8e-9 1.8e-7 1.1e-5 1.1e-5 1.2e-5 1.2e-5 1.2e-5   1418    1.0\n",
      "u_predict[355,1] 1.1e-5  4.7e-9 1.8e-7 1.1e-5 1.1e-5 1.1e-5 1.1e-5 1.2e-5   1418    1.0\n",
      "u_predict[356,1] 1.1e-5  4.6e-9 1.7e-7 1.1e-5 1.1e-5 1.1e-5 1.1e-5 1.1e-5   1418    1.0\n",
      "u_predict[357,1] 1.1e-5  4.5e-9 1.7e-7 1.0e-5 1.0e-5 1.1e-5 1.1e-5 1.1e-5   1419    1.0\n",
      "u_predict[358,1] 1.0e-5  4.4e-9 1.6e-7 1.0e-5 1.0e-5 1.0e-5 1.0e-5 1.1e-5   1419    1.0\n",
      "u_predict[359,1] 1.0e-5  4.3e-9 1.6e-7 9.7e-6 9.9e-6 1.0e-5 1.0e-5 1.0e-5   1419    1.0\n",
      "u_predict[360,1] 9.7e-6  4.2e-9 1.6e-7 9.5e-6 9.6e-6 9.7e-6 9.9e-6 1.0e-5   1419    1.0\n",
      "u_predict[361,1] 9.5e-6  4.1e-9 1.5e-7 9.2e-6 9.4e-6 9.5e-6 9.6e-6 9.8e-6   1419    1.0\n",
      "u_predict[362,1] 9.2e-6  4.0e-9 1.5e-7 8.9e-6 9.1e-6 9.2e-6 9.3e-6 9.5e-6   1419    1.0\n",
      "u_predict[363,1] 9.0e-6  3.9e-9 1.5e-7 8.7e-6 8.9e-6 9.0e-6 9.1e-6 9.2e-6   1419    1.0\n",
      "u_predict[364,1] 8.7e-6  3.8e-9 1.4e-7 8.5e-6 8.6e-6 8.7e-6 8.8e-6 9.0e-6   1419    1.0\n",
      "u_predict[365,1] 8.5e-6  3.7e-9 1.4e-7 8.2e-6 8.4e-6 8.5e-6 8.6e-6 8.7e-6   1419    1.0\n",
      "u_predict[366,1] 8.2e-6  3.6e-9 1.3e-7 8.0e-6 8.1e-6 8.2e-6 8.3e-6 8.5e-6   1419    1.0\n",
      "u_predict[367,1] 8.0e-6  3.5e-9 1.3e-7 7.8e-6 7.9e-6 8.0e-6 8.1e-6 8.3e-6   1418    1.0\n",
      "u_predict[368,1] 7.8e-6  3.4e-9 1.3e-7 7.6e-6 7.7e-6 7.8e-6 7.9e-6 8.0e-6   1418    1.0\n",
      "u_predict[369,1] 7.6e-6  3.3e-9 1.2e-7 7.3e-6 7.5e-6 7.6e-6 7.7e-6 7.8e-6   1418    1.0\n",
      "u_predict[370,1] 7.4e-6  3.2e-9 1.2e-7 7.1e-6 7.3e-6 7.4e-6 7.4e-6 7.6e-6   1418    1.0\n",
      "u_predict[371,1] 7.2e-6  3.1e-9 1.2e-7 6.9e-6 7.1e-6 7.1e-6 7.2e-6 7.4e-6   1418    1.0\n",
      "u_predict[372,1] 7.0e-6  3.1e-9 1.2e-7 6.7e-6 6.9e-6 6.9e-6 7.0e-6 7.2e-6   1418    1.0\n",
      "u_predict[373,1] 6.8e-6  3.0e-9 1.1e-7 6.6e-6 6.7e-6 6.8e-6 6.8e-6 7.0e-6   1418    1.0\n",
      "u_predict[374,1] 6.6e-6  2.9e-9 1.1e-7 6.4e-6 6.5e-6 6.6e-6 6.6e-6 6.8e-6   1417    1.0\n",
      "u_predict[375,1] 6.4e-6  2.8e-9 1.1e-7 6.2e-6 6.3e-6 6.4e-6 6.5e-6 6.6e-6   1417    1.0\n",
      "u_predict[376,1] 6.2e-6  2.8e-9 1.0e-7 6.0e-6 6.1e-6 6.2e-6 6.3e-6 6.4e-6   1417    1.0\n",
      "u_predict[377,1] 6.0e-6  2.7e-9 1.0e-7 5.8e-6 6.0e-6 6.0e-6 6.1e-6 6.2e-6   1417    1.0\n",
      "u_predict[378,1] 5.9e-6  2.6e-9 9.9e-8 5.7e-6 5.8e-6 5.9e-6 5.9e-6 6.1e-6   1417    1.0\n",
      "u_predict[379,1] 5.7e-6  2.6e-9 9.7e-8 5.5e-6 5.6e-6 5.7e-6 5.8e-6 5.9e-6   1417    1.0\n",
      "u_predict[380,1] 5.5e-6  2.5e-9 9.4e-8 5.4e-6 5.5e-6 5.5e-6 5.6e-6 5.7e-6   1417    1.0\n",
      "u_predict[381,1] 5.4e-6  2.4e-9 9.2e-8 5.2e-6 5.3e-6 5.4e-6 5.4e-6 5.6e-6   1416    1.0\n",
      "u_predict[382,1] 5.2e-6  2.4e-9 9.0e-8 5.1e-6 5.2e-6 5.2e-6 5.3e-6 5.4e-6   1416    1.0\n",
      "u_predict[383,1] 5.1e-6  2.3e-9 8.8e-8 4.9e-6 5.0e-6 5.1e-6 5.1e-6 5.3e-6   1416    1.0\n",
      "u_predict[384,1] 4.9e-6  2.3e-9 8.6e-8 4.8e-6 4.9e-6 4.9e-6 5.0e-6 5.1e-6   1416    1.0\n",
      "u_predict[385,1] 4.8e-6  2.2e-9 8.4e-8 4.7e-6 4.8e-6 4.8e-6 4.9e-6 5.0e-6   1416    1.0\n",
      "u_predict[386,1] 4.7e-6  2.2e-9 8.2e-8 4.5e-6 4.6e-6 4.7e-6 4.7e-6 4.8e-6   1416    1.0\n",
      "u_predict[387,1] 4.5e-6  2.1e-9 8.0e-8 4.4e-6 4.5e-6 4.5e-6 4.6e-6 4.7e-6   1416    1.0\n",
      "u_predict[388,1] 4.4e-6  2.1e-9 7.8e-8 4.3e-6 4.4e-6 4.4e-6 4.5e-6 4.6e-6   1416    1.0\n",
      "u_predict[389,1] 4.3e-6  2.0e-9 7.6e-8 4.2e-6 4.2e-6 4.3e-6 4.3e-6 4.4e-6   1416    1.0\n",
      "u_predict[390,1] 4.2e-6  2.0e-9 7.4e-8 4.0e-6 4.1e-6 4.2e-6 4.2e-6 4.3e-6   1416    1.0\n",
      "u_predict[391,1] 4.1e-6  1.9e-9 7.2e-8 3.9e-6 4.0e-6 4.1e-6 4.1e-6 4.2e-6   1416    1.0\n",
      "u_predict[392,1] 4.0e-6  1.9e-9 7.0e-8 3.8e-6 3.9e-6 3.9e-6 4.0e-6 4.1e-6   1416    1.0\n",
      "u_predict[393,1] 3.8e-6  1.8e-9 6.9e-8 3.7e-6 3.8e-6 3.8e-6 3.9e-6 4.0e-6   1416    1.0\n",
      "u_predict[394,1] 3.7e-6  1.8e-9 6.7e-8 3.6e-6 3.7e-6 3.7e-6 3.8e-6 3.9e-6   1416    1.0\n",
      "u_predict[395,1] 3.6e-6  1.7e-9 6.5e-8 3.5e-6 3.6e-6 3.6e-6 3.7e-6 3.8e-6   1417    1.0\n",
      "u_predict[396,1] 3.5e-6  1.7e-9 6.4e-8 3.4e-6 3.5e-6 3.5e-6 3.6e-6 3.7e-6   1417    1.0\n",
      "u_predict[397,1] 3.4e-6  1.7e-9 6.2e-8 3.3e-6 3.4e-6 3.4e-6 3.5e-6 3.6e-6   1417    1.0\n",
      "u_predict[398,1] 3.3e-6  1.6e-9 6.1e-8 3.2e-6 3.3e-6 3.3e-6 3.4e-6 3.5e-6   1417    1.0\n",
      "u_predict[399,1] 3.3e-6  1.6e-9 5.9e-8 3.1e-6 3.2e-6 3.2e-6 3.3e-6 3.4e-6   1417    1.0\n",
      "u_predict[400,1] 3.2e-6  1.5e-9 5.8e-8 3.1e-6 3.1e-6 3.2e-6 3.2e-6 3.3e-6   1417    1.0\n",
      "u_predict[401,1] 3.1e-6  1.5e-9 5.6e-8 3.0e-6 3.0e-6 3.1e-6 3.1e-6 3.2e-6   1418    1.0\n",
      "u_predict[402,1] 3.0e-6  1.5e-9 5.5e-8 2.9e-6 3.0e-6 3.0e-6 3.0e-6 3.1e-6   1418    1.0\n",
      "u_predict[403,1] 2.9e-6  1.4e-9 5.3e-8 2.8e-6 2.9e-6 2.9e-6 2.9e-6 3.0e-6   1418    1.0\n",
      "u_predict[404,1] 2.8e-6  1.4e-9 5.2e-8 2.7e-6 2.8e-6 2.8e-6 2.9e-6 2.9e-6   1418    1.0\n",
      "u_predict[405,1] 2.8e-6  1.3e-9 5.1e-8 2.7e-6 2.7e-6 2.7e-6 2.8e-6 2.9e-6   1418    1.0\n",
      "u_predict[406,1] 2.7e-6  1.3e-9 4.9e-8 2.6e-6 2.6e-6 2.7e-6 2.7e-6 2.8e-6   1419    1.0\n",
      "u_predict[407,1] 2.6e-6  1.3e-9 4.8e-8 2.5e-6 2.6e-6 2.6e-6 2.6e-6 2.7e-6   1419    1.0\n",
      "u_predict[408,1] 2.5e-6  1.2e-9 4.7e-8 2.4e-6 2.5e-6 2.5e-6 2.6e-6 2.6e-6   1419    1.0\n",
      "u_predict[409,1] 2.5e-6  1.2e-9 4.6e-8 2.4e-6 2.4e-6 2.5e-6 2.5e-6 2.5e-6   1419    1.0\n",
      "u_predict[410,1] 2.4e-6  1.2e-9 4.4e-8 2.3e-6 2.4e-6 2.4e-6 2.4e-6 2.5e-6   1419    1.0\n",
      "u_predict[411,1] 2.3e-6  1.1e-9 4.3e-8 2.2e-6 2.3e-6 2.3e-6 2.4e-6 2.4e-6   1419    1.0\n",
      "u_predict[412,1] 2.3e-6  1.1e-9 4.2e-8 2.2e-6 2.2e-6 2.3e-6 2.3e-6 2.3e-6   1420    1.0\n",
      "u_predict[413,1] 2.2e-6  1.1e-9 4.1e-8 2.1e-6 2.2e-6 2.2e-6 2.2e-6 2.3e-6   1420    1.0\n",
      "u_predict[1,2]   3.9e-8 9.0e-123.3e-10 3.8e-8 3.9e-8 3.9e-8 3.9e-8 3.9e-8   1366    1.0\n",
      "u_predict[2,2]   1.5e-7 3.7e-11 1.4e-9 1.5e-7 1.5e-7 1.5e-7 1.5e-7 1.6e-7   1366    1.0\n",
      "u_predict[3,2]   3.6e-7 8.8e-11 3.3e-9 3.6e-7 3.6e-7 3.6e-7 3.6e-7 3.7e-7   1367    1.0\n",
      "u_predict[4,2]   6.8e-7 1.6e-10 6.1e-9 6.7e-7 6.8e-7 6.8e-7 6.9e-7 6.9e-7   1369    1.0\n",
      "u_predict[5,2]   1.1e-6 2.7e-10 9.9e-9 1.1e-6 1.1e-6 1.1e-6 1.1e-6 1.2e-6   1371    1.0\n",
      "u_predict[6,2]   1.8e-6 4.0e-10 1.5e-8 1.7e-6 1.8e-6 1.8e-6 1.8e-6 1.8e-6   1373    1.0\n",
      "u_predict[7,2]   2.6e-6 5.7e-10 2.1e-8 2.6e-6 2.6e-6 2.6e-6 2.6e-6 2.6e-6   1376    1.0\n",
      "u_predict[8,2]   3.7e-6 7.9e-10 2.9e-8 3.6e-6 3.7e-6 3.7e-6 3.7e-6 3.7e-6   1379    1.0\n",
      "u_predict[9,2]   5.1e-6  1.0e-9 3.9e-8 5.0e-6 5.1e-6 5.1e-6 5.1e-6 5.2e-6   1382    1.0\n",
      "u_predict[10,2]  6.9e-6  1.3e-9 5.0e-8 6.8e-6 6.8e-6 6.9e-6 6.9e-6 6.9e-6   1387    1.0\n",
      "u_predict[11,2]  9.1e-6  1.7e-9 6.3e-8 9.0e-6 9.0e-6 9.1e-6 9.1e-6 9.2e-6   1392    1.0\n",
      "u_predict[12,2]  1.2e-5  2.1e-9 7.9e-8 1.2e-5 1.2e-5 1.2e-5 1.2e-5 1.2e-5   1398    1.0\n",
      "u_predict[13,2]  1.5e-5  2.6e-9 9.7e-8 1.5e-5 1.5e-5 1.5e-5 1.5e-5 1.6e-5   1404    1.0\n",
      "u_predict[14,2]  2.0e-5  3.1e-9 1.2e-7 2.0e-5 2.0e-5 2.0e-5 2.0e-5 2.0e-5   1412    1.0\n",
      "u_predict[15,2]  2.5e-5  3.7e-9 1.4e-7 2.5e-5 2.5e-5 2.5e-5 2.5e-5 2.5e-5   1422    1.0\n",
      "u_predict[16,2]  3.2e-5  4.3e-9 1.6e-7 3.1e-5 3.2e-5 3.2e-5 3.2e-5 3.2e-5   1432    1.0\n",
      "u_predict[17,2]  4.0e-5  5.0e-9 1.9e-7 3.9e-5 4.0e-5 4.0e-5 4.0e-5 4.0e-5   1444    1.0\n",
      "u_predict[18,2]  5.0e-5  5.8e-9 2.2e-7 4.9e-5 5.0e-5 5.0e-5 5.0e-5 5.0e-5   1458    1.0\n",
      "u_predict[19,2]  6.2e-5  6.6e-9 2.5e-7 6.1e-5 6.2e-5 6.2e-5 6.2e-5 6.2e-5   1473    1.0\n",
      "u_predict[20,2]  7.6e-5  7.4e-9 2.9e-7 7.6e-5 7.6e-5 7.6e-5 7.6e-5 7.7e-5   1489    1.0\n",
      "u_predict[21,2]  9.4e-5  8.3e-9 3.2e-7 9.3e-5 9.3e-5 9.4e-5 9.4e-5 9.4e-5   1503    1.0\n",
      "u_predict[22,2]  1.1e-4  9.3e-9 3.6e-7 1.1e-4 1.1e-4 1.1e-4 1.1e-4 1.2e-4   1508    1.0\n",
      "u_predict[23,2]  1.4e-4  1.0e-8 4.0e-7 1.4e-4 1.4e-4 1.4e-4 1.4e-4 1.4e-4   1510    1.0\n",
      "u_predict[24,2]  1.7e-4  1.2e-8 4.5e-7 1.7e-4 1.7e-4 1.7e-4 1.7e-4 1.7e-4   1508    1.0\n",
      "u_predict[25,2]  2.0e-4  1.3e-8 5.0e-7 2.0e-4 2.0e-4 2.0e-4 2.0e-4 2.0e-4   1500    1.0\n",
      "u_predict[26,2]  2.4e-4  1.5e-8 5.6e-7 2.4e-4 2.4e-4 2.4e-4 2.4e-4 2.4e-4   1488    1.0\n",
      "u_predict[27,2]  2.8e-4  1.7e-8 6.4e-7 2.8e-4 2.8e-4 2.8e-4 2.9e-4 2.9e-4   1471    1.0\n",
      "u_predict[28,2]  3.4e-4  1.9e-8 7.2e-7 3.3e-4 3.3e-4 3.4e-4 3.4e-4 3.4e-4   1452    1.0\n",
      "u_predict[29,2]  3.9e-4  2.2e-8 8.1e-7 3.9e-4 3.9e-4 3.9e-4 3.9e-4 3.9e-4   1432    1.0\n",
      "u_predict[30,2]  4.6e-4  2.5e-8 9.2e-7 4.5e-4 4.5e-4 4.6e-4 4.6e-4 4.6e-4   1412    1.0\n",
      "u_predict[31,2]  5.3e-4  2.8e-8 1.0e-6 5.2e-4 5.2e-4 5.3e-4 5.3e-4 5.3e-4   1395    1.0\n",
      "u_predict[32,2]  6.0e-4  3.2e-8 1.2e-6 6.0e-4 6.0e-4 6.0e-4 6.0e-4 6.0e-4   1380    1.0\n",
      "u_predict[33,2]  6.9e-4  3.6e-8 1.3e-6 6.8e-4 6.9e-4 6.9e-4 6.9e-4 6.9e-4   1368    1.0\n",
      "u_predict[34,2]  7.8e-4  4.1e-8 1.5e-6 7.7e-4 7.8e-4 7.8e-4 7.8e-4 7.8e-4   1358    1.0\n",
      "u_predict[35,2]  8.7e-4  4.6e-8 1.7e-6 8.7e-4 8.7e-4 8.7e-4 8.7e-4 8.8e-4   1351    1.0\n",
      "u_predict[36,2]  9.8e-4  5.1e-8 1.9e-6 9.7e-4 9.8e-4 9.8e-4 9.8e-4 9.8e-4   1347    1.0\n",
      "u_predict[37,2]  1.1e-3  5.6e-8 2.1e-6 1.1e-3 1.1e-3 1.1e-3 1.1e-3 1.1e-3   1345    1.0\n",
      "u_predict[38,2]  1.2e-3  6.2e-8 2.3e-6 1.2e-3 1.2e-3 1.2e-3 1.2e-3 1.2e-3   1344    1.0\n",
      "u_predict[39,2]  1.3e-3  6.9e-8 2.5e-6 1.3e-3 1.3e-3 1.3e-3 1.3e-3 1.3e-3   1345    1.0\n",
      "u_predict[40,2]  1.5e-3  7.5e-8 2.8e-6 1.4e-3 1.5e-3 1.5e-3 1.5e-3 1.5e-3   1347    1.0\n",
      "u_predict[41,2]  1.6e-3  8.2e-8 3.0e-6 1.6e-3 1.6e-3 1.6e-3 1.6e-3 1.6e-3   1351    1.0\n",
      "u_predict[42,2]  1.7e-3  8.9e-8 3.3e-6 1.7e-3 1.7e-3 1.7e-3 1.7e-3 1.7e-3   1355    1.0\n",
      "u_predict[43,2]  1.9e-3  9.6e-8 3.6e-6 1.9e-3 1.9e-3 1.9e-3 1.9e-3 1.9e-3   1360    1.0\n",
      "u_predict[44,2]  2.0e-3  1.0e-7 3.8e-6 2.0e-3 2.0e-3 2.0e-3 2.0e-3 2.0e-3   1365    1.0\n",
      "u_predict[45,2]  2.2e-3  1.1e-7 4.2e-6 2.2e-3 2.2e-3 2.2e-3 2.2e-3 2.2e-3   1370    1.0\n",
      "u_predict[46,2]  2.3e-3  1.2e-7 4.5e-6 2.3e-3 2.3e-3 2.3e-3 2.3e-3 2.3e-3   1376    1.0\n",
      "u_predict[47,2]  2.5e-3  1.3e-7 4.8e-6 2.5e-3 2.5e-3 2.5e-3 2.5e-3 2.5e-3   1382    1.0\n",
      "u_predict[48,2]  2.6e-3  1.4e-7 5.1e-6 2.6e-3 2.6e-3 2.6e-3 2.6e-3 2.6e-3   1388    1.0\n",
      "u_predict[49,2]  2.8e-3  1.5e-7 5.5e-6 2.8e-3 2.8e-3 2.8e-3 2.8e-3 2.8e-3   1393    1.0\n",
      "u_predict[50,2]  3.0e-3  1.6e-7 5.8e-6 2.9e-3 3.0e-3 3.0e-3 3.0e-3 3.0e-3   1399    1.0\n",
      "u_predict[51,2]  3.1e-3  1.7e-7 6.2e-6 3.1e-3 3.1e-3 3.1e-3 3.1e-3 3.1e-3   1404    1.0\n",
      "u_predict[52,2]  3.3e-3  1.7e-7 6.6e-6 3.3e-3 3.3e-3 3.3e-3 3.3e-3 3.3e-3   1410    1.0\n",
      "u_predict[53,2]  3.5e-3  1.8e-7 6.9e-6 3.5e-3 3.5e-3 3.5e-3 3.5e-3 3.5e-3   1415    1.0\n",
      "u_predict[54,2]  3.6e-3  1.9e-7 7.3e-6 3.6e-3 3.6e-3 3.6e-3 3.6e-3 3.7e-3   1420    1.0\n",
      "u_predict[55,2]  3.8e-3  2.0e-7 7.7e-6 3.8e-3 3.8e-3 3.8e-3 3.8e-3 3.8e-3   1424    1.0\n",
      "u_predict[56,2]  4.0e-3  2.1e-7 8.1e-6 4.0e-3 4.0e-3 4.0e-3 4.0e-3 4.0e-3   1429    1.0\n",
      "u_predict[57,2]  4.2e-3  2.3e-7 8.5e-6 4.2e-3 4.2e-3 4.2e-3 4.2e-3 4.2e-3   1433    1.0\n",
      "u_predict[58,2]  4.3e-3  2.4e-7 8.9e-6 4.3e-3 4.3e-3 4.3e-3 4.4e-3 4.4e-3   1437    1.0\n",
      "u_predict[59,2]  4.5e-3  2.5e-7 9.3e-6 4.5e-3 4.5e-3 4.5e-3 4.5e-3 4.5e-3   1441    1.0\n",
      "u_predict[60,2]  4.7e-3  2.6e-7 9.7e-6 4.7e-3 4.7e-3 4.7e-3 4.7e-3 4.7e-3   1445    1.0\n",
      "u_predict[61,2]  4.9e-3  2.7e-7 1.0e-5 4.9e-3 4.9e-3 4.9e-3 4.9e-3 4.9e-3   1448    1.0\n",
      "u_predict[62,2]  5.1e-3  2.8e-7 1.1e-5 5.0e-3 5.0e-3 5.1e-3 5.1e-3 5.1e-3   1451    1.0\n",
      "u_predict[63,2]  5.2e-3  2.9e-7 1.1e-5 5.2e-3 5.2e-3 5.2e-3 5.2e-3 5.3e-3   1454    1.0\n",
      "u_predict[64,2]  5.4e-3  3.0e-7 1.1e-5 5.4e-3 5.4e-3 5.4e-3 5.4e-3 5.4e-3   1457    1.0\n",
      "u_predict[65,2]  5.6e-3  3.1e-7 1.2e-5 5.6e-3 5.6e-3 5.6e-3 5.6e-3 5.6e-3   1460    1.0\n",
      "u_predict[66,2]  5.8e-3  3.2e-7 1.2e-5 5.7e-3 5.7e-3 5.8e-3 5.8e-3 5.8e-3   1462    1.0\n",
      "u_predict[67,2]  5.9e-3  3.3e-7 1.3e-5 5.9e-3 5.9e-3 5.9e-3 5.9e-3 6.0e-3   1465    1.0\n",
      "u_predict[68,2]  6.1e-3  3.4e-7 1.3e-5 6.1e-3 6.1e-3 6.1e-3 6.1e-3 6.1e-3   1467    1.0\n",
      "u_predict[69,2]  6.3e-3  3.5e-7 1.3e-5 6.3e-3 6.3e-3 6.3e-3 6.3e-3 6.3e-3   1469    1.0\n",
      "u_predict[70,2]  6.4e-3  3.6e-7 1.4e-5 6.4e-3 6.4e-3 6.4e-3 6.5e-3 6.5e-3   1471    1.0\n",
      "u_predict[71,2]  6.6e-3  3.7e-7 1.4e-5 6.6e-3 6.6e-3 6.6e-3 6.6e-3 6.6e-3   1473    1.0\n",
      "u_predict[72,2]  6.8e-3  3.8e-7 1.5e-5 6.8e-3 6.8e-3 6.8e-3 6.8e-3 6.8e-3   1475    1.0\n",
      "u_predict[73,2]  7.0e-3  3.9e-7 1.5e-5 6.9e-3 6.9e-3 7.0e-3 7.0e-3 7.0e-3   1477    1.0\n",
      "u_predict[74,2]  7.1e-3  4.0e-7 1.5e-5 7.1e-3 7.1e-3 7.1e-3 7.1e-3 7.2e-3   1479    1.0\n",
      "u_predict[75,2]  7.3e-3  4.1e-7 1.6e-5 7.3e-3 7.3e-3 7.3e-3 7.3e-3 7.3e-3   1480    1.0\n",
      "u_predict[76,2]  7.4e-3  4.2e-7 1.6e-5 7.4e-3 7.4e-3 7.4e-3 7.5e-3 7.5e-3   1482    1.0\n",
      "u_predict[77,2]  7.6e-3  4.3e-7 1.7e-5 7.6e-3 7.6e-3 7.6e-3 7.6e-3 7.6e-3   1483    1.0\n",
      "u_predict[78,2]  7.8e-3  4.4e-7 1.7e-5 7.7e-3 7.8e-3 7.8e-3 7.8e-3 7.8e-3   1484    1.0\n",
      "u_predict[79,2]  7.9e-3  4.5e-7 1.7e-5 7.9e-3 7.9e-3 7.9e-3 7.9e-3 8.0e-3   1486    1.0\n",
      "u_predict[80,2]  8.1e-3  4.6e-7 1.8e-5 8.0e-3 8.1e-3 8.1e-3 8.1e-3 8.1e-3   1487    1.0\n",
      "u_predict[81,2]  8.2e-3  4.7e-7 1.8e-5 8.2e-3 8.2e-3 8.2e-3 8.3e-3 8.3e-3   1488    1.0\n",
      "u_predict[82,2]  8.4e-3  4.8e-7 1.8e-5 8.4e-3 8.4e-3 8.4e-3 8.4e-3 8.4e-3   1489    1.0\n",
      "u_predict[83,2]  8.5e-3  4.9e-7 1.9e-5 8.5e-3 8.5e-3 8.5e-3 8.6e-3 8.6e-3   1490    1.0\n",
      "u_predict[84,2]  8.7e-3  5.0e-7 1.9e-5 8.7e-3 8.7e-3 8.7e-3 8.7e-3 8.7e-3   1491    1.0\n",
      "u_predict[85,2]  8.8e-3  5.1e-7 2.0e-5 8.8e-3 8.8e-3 8.8e-3 8.9e-3 8.9e-3   1492    1.0\n",
      "u_predict[86,2]  9.0e-3  5.1e-7 2.0e-5 8.9e-3 9.0e-3 9.0e-3 9.0e-3 9.0e-3   1493    1.0\n",
      "u_predict[87,2]  9.1e-3  5.2e-7 2.0e-5 9.1e-3 9.1e-3 9.1e-3 9.1e-3 9.2e-3   1493    1.0\n",
      "u_predict[88,2]  9.3e-3  5.3e-7 2.1e-5 9.2e-3 9.3e-3 9.3e-3 9.3e-3 9.3e-3   1494    1.0\n",
      "u_predict[89,2]  9.4e-3  5.4e-7 2.1e-5 9.4e-3 9.4e-3 9.4e-3 9.4e-3 9.4e-3   1495    1.0\n",
      "u_predict[90,2]  9.5e-3  5.5e-7 2.1e-5 9.5e-3 9.5e-3 9.5e-3 9.6e-3 9.6e-3   1495    1.0\n",
      "u_predict[91,2]  9.7e-3  5.6e-7 2.2e-5 9.6e-3 9.7e-3 9.7e-3 9.7e-3 9.7e-3   1496    1.0\n",
      "u_predict[92,2]  9.8e-3  5.6e-7 2.2e-5 9.8e-3 9.8e-3 9.8e-3 9.8e-3 9.9e-3   1497    1.0\n",
      "u_predict[93,2]  9.9e-3  5.7e-7 2.2e-5 9.9e-3 9.9e-3 9.9e-310.0e-310.0e-3   1497    1.0\n",
      "u_predict[94,2]    0.01  5.8e-7 2.2e-5   0.01   0.01   0.01   0.01   0.01   1498    1.0\n",
      "u_predict[95,2]    0.01  5.9e-7 2.3e-5   0.01   0.01   0.01   0.01   0.01   1498    1.0\n",
      "u_predict[96,2]    0.01  6.0e-7 2.3e-5   0.01   0.01   0.01   0.01   0.01   1499    1.0\n",
      "u_predict[97,2]    0.01  6.0e-7 2.3e-5   0.01   0.01   0.01   0.01   0.01   1499    1.0\n",
      "u_predict[98,2]    0.01  6.1e-7 2.4e-5   0.01   0.01   0.01   0.01   0.01   1499    1.0\n",
      "u_predict[99,2]    0.01  6.2e-7 2.4e-5   0.01   0.01   0.01   0.01   0.01   1500    1.0\n",
      "u_predict[100,2]   0.01  6.2e-7 2.4e-5   0.01   0.01   0.01   0.01   0.01   1500    1.0\n",
      "u_predict[101,2]   0.01  6.3e-7 2.4e-5   0.01   0.01   0.01   0.01   0.01   1500    1.0\n",
      "u_predict[102,2]   0.01  6.4e-7 2.5e-5   0.01   0.01   0.01   0.01   0.01   1501    1.0\n",
      "u_predict[103,2]   0.01  6.4e-7 2.5e-5   0.01   0.01   0.01   0.01   0.01   1501    1.0\n",
      "u_predict[104,2]   0.01  6.5e-7 2.5e-5   0.01   0.01   0.01   0.01   0.01   1501    1.0\n",
      "u_predict[105,2]   0.01  6.6e-7 2.5e-5   0.01   0.01   0.01   0.01   0.01   1501    1.0\n",
      "u_predict[106,2]   0.01  6.6e-7 2.6e-5   0.01   0.01   0.01   0.01   0.01   1502    1.0\n",
      "u_predict[107,2]   0.01  6.7e-7 2.6e-5   0.01   0.01   0.01   0.01   0.01   1502    1.0\n",
      "u_predict[108,2]   0.01  6.8e-7 2.6e-5   0.01   0.01   0.01   0.01   0.01   1502    1.0\n",
      "u_predict[109,2]   0.01  6.8e-7 2.6e-5   0.01   0.01   0.01   0.01   0.01   1502    1.0\n",
      "u_predict[110,2]   0.01  6.9e-7 2.7e-5   0.01   0.01   0.01   0.01   0.01   1502    1.0\n",
      "u_predict[111,2]   0.01  6.9e-7 2.7e-5   0.01   0.01   0.01   0.01   0.01   1502    1.0\n",
      "u_predict[112,2]   0.01  7.0e-7 2.7e-5   0.01   0.01   0.01   0.01   0.01   1502    1.0\n",
      "u_predict[113,2]   0.01  7.1e-7 2.7e-5   0.01   0.01   0.01   0.01   0.01   1502    1.0\n",
      "u_predict[114,2]   0.01  7.1e-7 2.8e-5   0.01   0.01   0.01   0.01   0.01   1502    1.0\n",
      "u_predict[115,2]   0.01  7.2e-7 2.8e-5   0.01   0.01   0.01   0.01   0.01   1502    1.0\n",
      "u_predict[116,2]   0.01  7.2e-7 2.8e-5   0.01   0.01   0.01   0.01   0.01   1503    1.0\n",
      "u_predict[117,2]   0.01  7.3e-7 2.8e-5   0.01   0.01   0.01   0.01   0.01   1503    1.0\n",
      "u_predict[118,2]   0.01  7.3e-7 2.8e-5   0.01   0.01   0.01   0.01   0.01   1503    1.0\n",
      "u_predict[119,2]   0.01  7.4e-7 2.9e-5   0.01   0.01   0.01   0.01   0.01   1503    1.0\n",
      "u_predict[120,2]   0.01  7.4e-7 2.9e-5   0.01   0.01   0.01   0.01   0.01   1502    1.0\n",
      "u_predict[121,2]   0.01  7.5e-7 2.9e-5   0.01   0.01   0.01   0.01   0.01   1502    1.0\n",
      "u_predict[122,2]   0.01  7.5e-7 2.9e-5   0.01   0.01   0.01   0.01   0.01   1502    1.0\n",
      "u_predict[123,2]   0.01  7.6e-7 2.9e-5   0.01   0.01   0.01   0.01   0.01   1502    1.0\n",
      "u_predict[124,2]   0.01  7.6e-7 3.0e-5   0.01   0.01   0.01   0.01   0.01   1502    1.0\n",
      "u_predict[125,2]   0.01  7.7e-7 3.0e-5   0.01   0.01   0.01   0.01   0.01   1502    1.0\n",
      "u_predict[126,2]   0.01  7.7e-7 3.0e-5   0.01   0.01   0.01   0.01   0.01   1502    1.0\n",
      "u_predict[127,2]   0.01  7.8e-7 3.0e-5   0.01   0.01   0.01   0.01   0.01   1502    1.0\n",
      "u_predict[128,2]   0.01  7.8e-7 3.0e-5   0.01   0.01   0.01   0.01   0.01   1502    1.0\n",
      "u_predict[129,2]   0.01  7.9e-7 3.0e-5   0.01   0.01   0.01   0.01   0.01   1502    1.0\n",
      "u_predict[130,2]   0.01  7.9e-7 3.1e-5   0.01   0.01   0.01   0.01   0.01   1502    1.0\n",
      "u_predict[131,2]   0.01  7.9e-7 3.1e-5   0.01   0.01   0.01   0.01   0.01   1502    1.0\n",
      "u_predict[132,2]   0.01  8.0e-7 3.1e-5   0.01   0.01   0.01   0.01   0.01   1501    1.0\n",
      "u_predict[133,2]   0.01  8.0e-7 3.1e-5   0.01   0.01   0.01   0.01   0.01   1501    1.0\n",
      "u_predict[134,2]   0.01  8.1e-7 3.1e-5   0.01   0.01   0.01   0.01   0.01   1501    1.0\n",
      "u_predict[135,2]   0.01  8.1e-7 3.1e-5   0.01   0.01   0.01   0.01   0.01   1501    1.0\n",
      "u_predict[136,2]   0.01  8.1e-7 3.2e-5   0.01   0.01   0.01   0.01   0.01   1501    1.0\n",
      "u_predict[137,2]   0.01  8.2e-7 3.2e-5   0.01   0.01   0.01   0.01   0.01   1501    1.0\n",
      "u_predict[138,2]   0.01  8.2e-7 3.2e-5   0.01   0.01   0.01   0.01   0.01   1501    1.0\n",
      "u_predict[139,2]   0.01  8.2e-7 3.2e-5   0.01   0.01   0.01   0.01   0.01   1500    1.0\n",
      "u_predict[140,2]   0.01  8.3e-7 3.2e-5   0.01   0.01   0.01   0.01   0.01   1500    1.0\n",
      "u_predict[141,2]   0.01  8.3e-7 3.2e-5   0.01   0.01   0.01   0.01   0.01   1500    1.0\n",
      "u_predict[142,2]   0.01  8.3e-7 3.2e-5   0.01   0.01   0.01   0.01   0.01   1500    1.0\n",
      "u_predict[143,2]   0.01  8.4e-7 3.2e-5   0.01   0.01   0.01   0.01   0.01   1500    1.0\n",
      "u_predict[144,2]   0.01  8.4e-7 3.3e-5   0.01   0.01   0.01   0.01   0.01   1500    1.0\n",
      "u_predict[145,2]   0.01  8.4e-7 3.3e-5   0.01   0.01   0.01   0.01   0.01   1499    1.0\n",
      "u_predict[146,2]   0.01  8.5e-7 3.3e-5   0.01   0.01   0.01   0.01   0.01   1499    1.0\n",
      "u_predict[147,2]   0.01  8.5e-7 3.3e-5   0.01   0.01   0.01   0.01   0.01   1499    1.0\n",
      "u_predict[148,2]   0.01  8.5e-7 3.3e-5   0.01   0.01   0.01   0.01   0.01   1499    1.0\n",
      "u_predict[149,2]   0.01  8.6e-7 3.3e-5   0.01   0.01   0.01   0.01   0.01   1499    1.0\n",
      "u_predict[150,2]   0.01  8.6e-7 3.3e-5   0.01   0.01   0.01   0.01   0.01   1498    1.0\n",
      "u_predict[151,2]   0.01  8.6e-7 3.3e-5   0.01   0.01   0.01   0.01   0.01   1498    1.0\n",
      "u_predict[152,2]   0.01  8.7e-7 3.4e-5   0.01   0.01   0.01   0.01   0.01   1498    1.0\n",
      "u_predict[153,2]   0.01  8.7e-7 3.4e-5   0.01   0.01   0.01   0.01   0.01   1498    1.0\n",
      "u_predict[154,2]   0.01  8.7e-7 3.4e-5   0.01   0.01   0.01   0.01   0.01   1498    1.0\n",
      "u_predict[155,2]   0.01  8.7e-7 3.4e-5   0.01   0.01   0.01   0.01   0.01   1498    1.0\n",
      "u_predict[156,2]   0.01  8.8e-7 3.4e-5   0.01   0.01   0.01   0.01   0.01   1497    1.0\n",
      "u_predict[157,2]   0.01  8.8e-7 3.4e-5   0.01   0.01   0.01   0.01   0.01   1497    1.0\n",
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      "u_predict[293,2]   0.02  1.0e-6 3.8e-5   0.02   0.02   0.02   0.02   0.02   1475    1.0\n",
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      "u_predict[399,2]   0.02  1.0e-6 3.9e-5   0.02   0.02   0.02   0.02   0.02   1474    1.0\n",
      "u_predict[400,2]   0.02  1.0e-6 3.9e-5   0.02   0.02   0.02   0.02   0.02   1474    1.0\n",
      "u_predict[401,2]   0.02  1.0e-6 3.9e-5   0.02   0.02   0.02   0.02   0.02   1474    1.0\n",
      "u_predict[402,2]   0.02  1.0e-6 3.9e-5   0.02   0.02   0.02   0.02   0.02   1474    1.0\n",
      "u_predict[403,2]   0.02  1.0e-6 3.9e-5   0.02   0.02   0.02   0.02   0.02   1474    1.0\n",
      "u_predict[404,2]   0.02  1.0e-6 3.9e-5   0.02   0.02   0.02   0.02   0.02   1474    1.0\n",
      "u_predict[405,2]   0.02  1.0e-6 3.9e-5   0.02   0.02   0.02   0.02   0.02   1474    1.0\n",
      "u_predict[406,2]   0.02  1.0e-6 3.9e-5   0.02   0.02   0.02   0.02   0.02   1474    1.0\n",
      "u_predict[407,2]   0.02  1.0e-6 3.9e-5   0.02   0.02   0.02   0.02   0.02   1474    1.0\n",
      "u_predict[408,2]   0.02  1.0e-6 3.9e-5   0.02   0.02   0.02   0.02   0.02   1474    1.0\n",
      "u_predict[409,2]   0.02  1.0e-6 3.9e-5   0.02   0.02   0.02   0.02   0.02   1474    1.0\n",
      "u_predict[410,2]   0.02  1.0e-6 3.9e-5   0.02   0.02   0.02   0.02   0.02   1474    1.0\n",
      "u_predict[411,2]   0.02  1.0e-6 3.9e-5   0.02   0.02   0.02   0.02   0.02   1474    1.0\n",
      "u_predict[412,2]   0.02  1.0e-6 3.9e-5   0.02   0.02   0.02   0.02   0.02   1474    1.0\n",
      "u_predict[413,2]   0.02  1.0e-6 3.9e-5   0.02   0.02   0.02   0.02   0.02   1474    1.0\n",
      "u_predict[1,3]   3.0e-8 7.1e-122.6e-10 2.9e-8 3.0e-8 3.0e-8 3.0e-8 3.0e-8   1304    1.0\n",
      "u_predict[2,3]   1.2e-7 2.9e-11 1.1e-9 1.2e-7 1.2e-7 1.2e-7 1.2e-7 1.2e-7   1312    1.0\n",
      "u_predict[3,3]   2.8e-7 6.9e-11 2.5e-9 2.7e-7 2.8e-7 2.8e-7 2.8e-7 2.8e-7   1312    1.0\n",
      "u_predict[4,3]   5.2e-7 1.3e-10 4.7e-9 5.1e-7 5.2e-7 5.2e-7 5.3e-7 5.3e-7   1312    1.0\n",
      "u_predict[5,3]   8.8e-7 2.1e-10 7.6e-9 8.6e-7 8.7e-7 8.8e-7 8.8e-7 8.9e-7   1312    1.0\n",
      "u_predict[6,3]   1.4e-6 3.2e-10 1.2e-8 1.3e-6 1.3e-6 1.4e-6 1.4e-6 1.4e-6   1312    1.0\n",
      "u_predict[7,3]   2.0e-6 4.5e-10 1.6e-8 2.0e-6 2.0e-6 2.0e-6 2.0e-6 2.0e-6   1312    1.0\n",
      "u_predict[8,3]   2.8e-6 6.2e-10 2.3e-8 2.8e-6 2.8e-6 2.8e-6 2.8e-6 2.9e-6   1312    1.0\n",
      "u_predict[9,3]   3.9e-6 8.3e-10 3.0e-8 3.8e-6 3.9e-6 3.9e-6 3.9e-6 4.0e-6   1312    1.0\n",
      "u_predict[10,3]  5.3e-6  1.1e-9 3.9e-8 5.2e-6 5.2e-6 5.3e-6 5.3e-6 5.3e-6   1312    1.0\n",
      "u_predict[11,3]  7.0e-6  1.4e-9 4.9e-8 6.9e-6 6.9e-6 7.0e-6 7.0e-6 7.1e-6   1312    1.0\n",
      "u_predict[12,3]  9.1e-6  1.7e-9 6.1e-8 9.0e-6 9.1e-6 9.1e-6 9.2e-6 9.3e-6   1312    1.0\n",
      "u_predict[13,3]  1.2e-5  2.1e-9 7.5e-8 1.2e-5 1.2e-5 1.2e-5 1.2e-5 1.2e-5   1312    1.0\n",
      "u_predict[14,3]  1.5e-5  2.5e-9 9.1e-8 1.5e-5 1.5e-5 1.5e-5 1.5e-5 1.5e-5   1313    1.0\n",
      "u_predict[15,3]  1.9e-5  3.0e-9 1.1e-7 1.9e-5 1.9e-5 1.9e-5 1.9e-5 2.0e-5   1313    1.0\n",
      "u_predict[16,3]  2.4e-5  3.5e-9 1.3e-7 2.4e-5 2.4e-5 2.4e-5 2.4e-5 2.5e-5   1314    1.0\n",
      "u_predict[17,3]  3.1e-5  4.1e-9 1.5e-7 3.0e-5 3.1e-5 3.1e-5 3.1e-5 3.1e-5   1314    1.0\n",
      "u_predict[18,3]  3.8e-5  4.8e-9 1.7e-7 3.8e-5 3.8e-5 3.8e-5 3.8e-5 3.9e-5   1315    1.0\n",
      "u_predict[19,3]  4.7e-5  5.5e-9 2.0e-7 4.7e-5 4.7e-5 4.7e-5 4.8e-5 4.8e-5   1315    1.0\n",
      "u_predict[20,3]  5.9e-5  6.3e-9 2.3e-7 5.8e-5 5.8e-5 5.9e-5 5.9e-5 5.9e-5   1315    1.0\n",
      "u_predict[21,3]  7.2e-5  7.1e-9 2.6e-7 7.1e-5 7.2e-5 7.2e-5 7.2e-5 7.2e-5   1315    1.0\n",
      "u_predict[22,3]  8.8e-5  8.0e-9 2.9e-7 8.7e-5 8.8e-5 8.8e-5 8.8e-5 8.8e-5   1315    1.0\n",
      "u_predict[23,3]  1.1e-4  9.0e-9 3.3e-7 1.1e-4 1.1e-4 1.1e-4 1.1e-4 1.1e-4   1314    1.0\n",
      "u_predict[24,3]  1.3e-4  1.0e-8 3.7e-7 1.3e-4 1.3e-4 1.3e-4 1.3e-4 1.3e-4   1313    1.0\n",
      "u_predict[25,3]  1.5e-4  1.2e-8 4.2e-7 1.5e-4 1.5e-4 1.5e-4 1.5e-4 1.6e-4   1312    1.0\n",
      "u_predict[26,3]  1.8e-4  1.3e-8 4.7e-7 1.8e-4 1.8e-4 1.8e-4 1.8e-4 1.9e-4   1311    1.0\n",
      "u_predict[27,3]  2.2e-4  1.5e-8 5.4e-7 2.2e-4 2.2e-4 2.2e-4 2.2e-4 2.2e-4   1311    1.0\n",
      "u_predict[28,3]  2.6e-4  1.7e-8 6.1e-7 2.6e-4 2.6e-4 2.6e-4 2.6e-4 2.6e-4   1312    1.0\n",
      "u_predict[29,3]  3.0e-4  1.9e-8 7.0e-7 3.0e-4 3.0e-4 3.0e-4 3.0e-4 3.0e-4   1314    1.0\n",
      "u_predict[30,3]  3.5e-4  2.2e-8 8.0e-7 3.5e-4 3.5e-4 3.5e-4 3.5e-4 3.5e-4   1317    1.0\n",
      "u_predict[31,3]  4.0e-4  2.5e-8 9.1e-7 4.0e-4 4.0e-4 4.0e-4 4.0e-4 4.1e-4   1317    1.0\n",
      "u_predict[32,3]  4.6e-4  2.8e-8 1.0e-6 4.6e-4 4.6e-4 4.6e-4 4.6e-4 4.6e-4   1318    1.0\n",
      "u_predict[33,3]  5.3e-4  3.2e-8 1.2e-6 5.2e-4 5.3e-4 5.3e-4 5.3e-4 5.3e-4   1321    1.0\n",
      "u_predict[34,3]  6.0e-4  3.6e-8 1.3e-6 5.9e-4 6.0e-4 6.0e-4 6.0e-4 6.0e-4   1324    1.0\n",
      "u_predict[35,3]  6.7e-4  4.0e-8 1.5e-6 6.7e-4 6.7e-4 6.7e-4 6.7e-4 6.7e-4   1328    1.0\n",
      "u_predict[36,3]  7.5e-4  4.5e-8 1.6e-6 7.5e-4 7.5e-4 7.5e-4 7.5e-4 7.5e-4   1334    1.0\n",
      "u_predict[37,3]  8.3e-4  5.0e-8 1.8e-6 8.3e-4 8.3e-4 8.3e-4 8.4e-4 8.4e-4   1339    1.0\n",
      "u_predict[38,3]  9.2e-4  5.5e-8 2.0e-6 9.2e-4 9.2e-4 9.2e-4 9.3e-4 9.3e-4   1346    1.0\n",
      "u_predict[39,3]  1.0e-3  6.0e-8 2.2e-6 1.0e-3 1.0e-3 1.0e-3 1.0e-3 1.0e-3   1352    1.0\n",
      "u_predict[40,3]  1.1e-3  6.6e-8 2.4e-6 1.1e-3 1.1e-3 1.1e-3 1.1e-3 1.1e-3   1359    1.0\n",
      "u_predict[41,3]  1.2e-3  7.1e-8 2.6e-6 1.2e-3 1.2e-3 1.2e-3 1.2e-3 1.2e-3   1367    1.0\n",
      "u_predict[42,3]  1.3e-3  7.8e-8 2.9e-6 1.3e-3 1.3e-3 1.3e-3 1.3e-3 1.3e-3   1374    1.0\n",
      "u_predict[43,3]  1.4e-3  8.4e-8 3.1e-6 1.4e-3 1.4e-3 1.4e-3 1.4e-3 1.4e-3   1381    1.0\n",
      "u_predict[44,3]  1.5e-3  9.0e-8 3.4e-6 1.5e-3 1.5e-3 1.5e-3 1.5e-3 1.6e-3   1388    1.0\n",
      "u_predict[45,3]  1.7e-3  9.7e-8 3.6e-6 1.7e-3 1.7e-3 1.7e-3 1.7e-3 1.7e-3   1396    1.0\n",
      "u_predict[46,3]  1.8e-3  1.0e-7 3.9e-6 1.8e-3 1.8e-3 1.8e-3 1.8e-3 1.8e-3   1403    1.0\n",
      "u_predict[47,3]  1.9e-3  1.1e-7 4.2e-6 1.9e-3 1.9e-3 1.9e-3 1.9e-3 1.9e-3   1409    1.0\n",
      "u_predict[48,3]  2.0e-3  1.2e-7 4.5e-6 2.0e-3 2.0e-3 2.0e-3 2.0e-3 2.0e-3   1416    1.0\n",
      "u_predict[49,3]  2.1e-3  1.3e-7 4.8e-6 2.1e-3 2.1e-3 2.1e-3 2.2e-3 2.2e-3   1422    1.0\n",
      "u_predict[50,3]  2.3e-3  1.3e-7 5.1e-6 2.3e-3 2.3e-3 2.3e-3 2.3e-3 2.3e-3   1428    1.0\n",
      "u_predict[51,3]  2.4e-3  1.4e-7 5.4e-6 2.4e-3 2.4e-3 2.4e-3 2.4e-3 2.4e-3   1434    1.0\n",
      "u_predict[52,3]  2.5e-3  1.5e-7 5.7e-6 2.5e-3 2.5e-3 2.5e-3 2.5e-3 2.5e-3   1439    1.0\n",
      "u_predict[53,3]  2.7e-3  1.6e-7 6.0e-6 2.7e-3 2.7e-3 2.7e-3 2.7e-3 2.7e-3   1444    1.0\n",
      "u_predict[54,3]  2.8e-3  1.7e-7 6.3e-6 2.8e-3 2.8e-3 2.8e-3 2.8e-3 2.8e-3   1449    1.0\n",
      "u_predict[55,3]  2.9e-3  1.7e-7 6.6e-6 2.9e-3 2.9e-3 2.9e-3 2.9e-3 2.9e-3   1454    1.0\n",
      "u_predict[56,3]  3.1e-3  1.8e-7 7.0e-6 3.1e-3 3.1e-3 3.1e-3 3.1e-3 3.1e-3   1458    1.0\n",
      "u_predict[57,3]  3.2e-3  1.9e-7 7.3e-6 3.2e-3 3.2e-3 3.2e-3 3.2e-3 3.2e-3   1462    1.0\n",
      "u_predict[58,3]  3.3e-3  2.0e-7 7.7e-6 3.3e-3 3.3e-3 3.3e-3 3.3e-3 3.4e-3   1466    1.0\n",
      "u_predict[59,3]  3.5e-3  2.1e-7 8.0e-6 3.5e-3 3.5e-3 3.5e-3 3.5e-3 3.5e-3   1470    1.0\n",
      "u_predict[60,3]  3.6e-3  2.2e-7 8.3e-6 3.6e-3 3.6e-3 3.6e-3 3.6e-3 3.6e-3   1473    1.0\n",
      "u_predict[61,3]  3.7e-3  2.3e-7 8.7e-6 3.7e-3 3.7e-3 3.7e-3 3.8e-3 3.8e-3   1477    1.0\n",
      "u_predict[62,3]  3.9e-3  2.3e-7 9.0e-6 3.9e-3 3.9e-3 3.9e-3 3.9e-3 3.9e-3   1480    1.0\n",
      "u_predict[63,3]  4.0e-3  2.4e-7 9.4e-6 4.0e-3 4.0e-3 4.0e-3 4.0e-3 4.0e-3   1483    1.0\n",
      "u_predict[64,3]  4.2e-3  2.5e-7 9.7e-6 4.1e-3 4.1e-3 4.2e-3 4.2e-3 4.2e-3   1485    1.0\n",
      "u_predict[65,3]  4.3e-3  2.6e-7 1.0e-5 4.3e-3 4.3e-3 4.3e-3 4.3e-3 4.3e-3   1488    1.0\n",
      "u_predict[66,3]  4.4e-3  2.7e-7 1.0e-5 4.4e-3 4.4e-3 4.4e-3 4.4e-3 4.4e-3   1491    1.0\n",
      "u_predict[67,3]  4.6e-3  2.8e-7 1.1e-5 4.5e-3 4.5e-3 4.6e-3 4.6e-3 4.6e-3   1493    1.0\n",
      "u_predict[68,3]  4.7e-3  2.9e-7 1.1e-5 4.7e-3 4.7e-3 4.7e-3 4.7e-3 4.7e-3   1495    1.0\n",
      "u_predict[69,3]  4.8e-3  2.9e-7 1.1e-5 4.8e-3 4.8e-3 4.8e-3 4.8e-3 4.8e-3   1498    1.0\n",
      "u_predict[70,3]  5.0e-3  3.0e-7 1.2e-5 4.9e-3 4.9e-3 5.0e-3 5.0e-3 5.0e-3   1500    1.0\n",
      "u_predict[71,3]  5.1e-3  3.1e-7 1.2e-5 5.1e-3 5.1e-3 5.1e-3 5.1e-3 5.1e-3   1502    1.0\n",
      "u_predict[72,3]  5.2e-3  3.2e-7 1.2e-5 5.2e-3 5.2e-3 5.2e-3 5.2e-3 5.2e-3   1503    1.0\n",
      "u_predict[73,3]  5.3e-3  3.3e-7 1.3e-5 5.3e-3 5.3e-3 5.3e-3 5.3e-3 5.4e-3   1505    1.0\n",
      "u_predict[74,3]  5.5e-3  3.4e-7 1.3e-5 5.4e-3 5.5e-3 5.5e-3 5.5e-3 5.5e-3   1507    1.0\n",
      "u_predict[75,3]  5.6e-3  3.4e-7 1.3e-5 5.6e-3 5.6e-3 5.6e-3 5.6e-3 5.6e-3   1509    1.0\n",
      "u_predict[76,3]  5.7e-3  3.5e-7 1.4e-5 5.7e-3 5.7e-3 5.7e-3 5.7e-3 5.7e-3   1510    1.0\n",
      "u_predict[77,3]  5.8e-3  3.6e-7 1.4e-5 5.8e-3 5.8e-3 5.8e-3 5.9e-3 5.9e-3   1512    1.0\n",
      "u_predict[78,3]  6.0e-3  3.7e-7 1.4e-5 5.9e-3 6.0e-3 6.0e-3 6.0e-3 6.0e-3   1513    1.0\n",
      "u_predict[79,3]  6.1e-3  3.8e-7 1.5e-5 6.1e-3 6.1e-3 6.1e-3 6.1e-3 6.1e-3   1515    1.0\n",
      "u_predict[80,3]  6.2e-3  3.8e-7 1.5e-5 6.2e-3 6.2e-3 6.2e-3 6.2e-3 6.2e-3   1516    1.0\n",
      "u_predict[81,3]  6.3e-3  3.9e-7 1.5e-5 6.3e-3 6.3e-3 6.3e-3 6.3e-3 6.4e-3   1517    1.0\n",
      "u_predict[82,3]  6.4e-3  4.0e-7 1.6e-5 6.4e-3 6.4e-3 6.4e-3 6.5e-3 6.5e-3   1518    1.0\n",
      "u_predict[83,3]  6.6e-3  4.1e-7 1.6e-5 6.5e-3 6.5e-3 6.6e-3 6.6e-3 6.6e-3   1520    1.0\n",
      "u_predict[84,3]  6.7e-3  4.1e-7 1.6e-5 6.6e-3 6.7e-3 6.7e-3 6.7e-3 6.7e-3   1521    1.0\n",
      "u_predict[85,3]  6.8e-3  4.2e-7 1.6e-5 6.8e-3 6.8e-3 6.8e-3 6.8e-3 6.8e-3   1522    1.0\n",
      "u_predict[86,3]  6.9e-3  4.3e-7 1.7e-5 6.9e-3 6.9e-3 6.9e-3 6.9e-3 6.9e-3   1523    1.0\n",
      "u_predict[87,3]  7.0e-3  4.4e-7 1.7e-5 7.0e-3 7.0e-3 7.0e-3 7.0e-3 7.0e-3   1524    1.0\n",
      "u_predict[88,3]  7.1e-3  4.4e-7 1.7e-5 7.1e-3 7.1e-3 7.1e-3 7.1e-3 7.2e-3   1525    1.0\n",
      "u_predict[89,3]  7.2e-3  4.5e-7 1.8e-5 7.2e-3 7.2e-3 7.2e-3 7.2e-3 7.3e-3   1526    1.0\n",
      "u_predict[90,3]  7.3e-3  4.6e-7 1.8e-5 7.3e-3 7.3e-3 7.3e-3 7.3e-3 7.4e-3   1527    1.0\n",
      "u_predict[91,3]  7.4e-3  4.6e-7 1.8e-5 7.4e-3 7.4e-3 7.4e-3 7.4e-3 7.5e-3   1528    1.0\n",
      "u_predict[92,3]  7.5e-3  4.7e-7 1.8e-5 7.5e-3 7.5e-3 7.5e-3 7.5e-3 7.6e-3   1528    1.0\n",
      "u_predict[93,3]  7.6e-3  4.8e-7 1.9e-5 7.6e-3 7.6e-3 7.6e-3 7.6e-3 7.7e-3   1529    1.0\n",
      "u_predict[94,3]  7.7e-3  4.8e-7 1.9e-5 7.7e-3 7.7e-3 7.7e-3 7.7e-3 7.8e-3   1530    1.0\n",
      "u_predict[95,3]  7.8e-3  4.9e-7 1.9e-5 7.8e-3 7.8e-3 7.8e-3 7.8e-3 7.9e-3   1531    1.0\n",
      "u_predict[96,3]  7.9e-3  4.9e-7 1.9e-5 7.9e-3 7.9e-3 7.9e-3 7.9e-3 8.0e-3   1532    1.0\n",
      "u_predict[97,3]  8.0e-3  5.0e-7 2.0e-5 8.0e-3 8.0e-3 8.0e-3 8.0e-3 8.1e-3   1532    1.0\n",
      "u_predict[98,3]  8.1e-3  5.1e-7 2.0e-5 8.1e-3 8.1e-3 8.1e-3 8.1e-3 8.2e-3   1533    1.0\n",
      "u_predict[99,3]  8.2e-3  5.1e-7 2.0e-5 8.2e-3 8.2e-3 8.2e-3 8.2e-3 8.2e-3   1534    1.0\n",
      "u_predict[100,3] 8.3e-3  5.2e-7 2.0e-5 8.3e-3 8.3e-3 8.3e-3 8.3e-3 8.3e-3   1534    1.0\n",
      "u_predict[101,3] 8.4e-3  5.2e-7 2.1e-5 8.3e-3 8.4e-3 8.4e-3 8.4e-3 8.4e-3   1535    1.0\n",
      "u_predict[102,3] 8.5e-3  5.3e-7 2.1e-5 8.4e-3 8.5e-3 8.5e-3 8.5e-3 8.5e-3   1535    1.0\n",
      "u_predict[103,3] 8.6e-3  5.4e-7 2.1e-5 8.5e-3 8.5e-3 8.6e-3 8.6e-3 8.6e-3   1536    1.0\n",
      "u_predict[104,3] 8.6e-3  5.4e-7 2.1e-5 8.6e-3 8.6e-3 8.6e-3 8.7e-3 8.7e-3   1537    1.0\n",
      "u_predict[105,3] 8.7e-3  5.5e-7 2.1e-5 8.7e-3 8.7e-3 8.7e-3 8.7e-3 8.8e-3   1537    1.0\n",
      "u_predict[106,3] 8.8e-3  5.5e-7 2.2e-5 8.8e-3 8.8e-3 8.8e-3 8.8e-3 8.8e-3   1538    1.0\n",
      "u_predict[107,3] 8.9e-3  5.6e-7 2.2e-5 8.8e-3 8.9e-3 8.9e-3 8.9e-3 8.9e-3   1538    1.0\n",
      "u_predict[108,3] 9.0e-3  5.6e-7 2.2e-5 8.9e-3 8.9e-3 9.0e-3 9.0e-3 9.0e-3   1539    1.0\n",
      "u_predict[109,3] 9.0e-3  5.7e-7 2.2e-5 9.0e-3 9.0e-3 9.0e-3 9.1e-3 9.1e-3   1539    1.0\n",
      "u_predict[110,3] 9.1e-3  5.7e-7 2.2e-5 9.1e-3 9.1e-3 9.1e-3 9.1e-3 9.2e-3   1539    1.0\n",
      "u_predict[111,3] 9.2e-3  5.8e-7 2.3e-5 9.1e-3 9.2e-3 9.2e-3 9.2e-3 9.2e-3   1540    1.0\n",
      "u_predict[112,3] 9.3e-3  5.8e-7 2.3e-5 9.2e-3 9.2e-3 9.3e-3 9.3e-3 9.3e-3   1540    1.0\n",
      "u_predict[113,3] 9.3e-3  5.9e-7 2.3e-5 9.3e-3 9.3e-3 9.3e-3 9.3e-3 9.4e-3   1541    1.0\n",
      "u_predict[114,3] 9.4e-3  5.9e-7 2.3e-5 9.4e-3 9.4e-3 9.4e-3 9.4e-3 9.5e-3   1541    1.0\n",
      "u_predict[115,3] 9.5e-3  5.9e-7 2.3e-5 9.4e-3 9.5e-3 9.5e-3 9.5e-3 9.5e-3   1541    1.0\n",
      "u_predict[116,3] 9.5e-3  6.0e-7 2.4e-5 9.5e-3 9.5e-3 9.5e-3 9.6e-3 9.6e-3   1542    1.0\n",
      "u_predict[117,3] 9.6e-3  6.0e-7 2.4e-5 9.6e-3 9.6e-3 9.6e-3 9.6e-3 9.7e-3   1542    1.0\n",
      "u_predict[118,3] 9.7e-3  6.1e-7 2.4e-5 9.6e-3 9.7e-3 9.7e-3 9.7e-3 9.7e-3   1542    1.0\n",
      "u_predict[119,3] 9.7e-3  6.1e-7 2.4e-5 9.7e-3 9.7e-3 9.7e-3 9.7e-3 9.8e-3   1543    1.0\n",
      "u_predict[120,3] 9.8e-3  6.2e-7 2.4e-5 9.7e-3 9.8e-3 9.8e-3 9.8e-3 9.8e-3   1543    1.0\n",
      "u_predict[121,3] 9.9e-3  6.2e-7 2.4e-5 9.8e-3 9.8e-3 9.9e-3 9.9e-3 9.9e-3   1543    1.0\n",
      "u_predict[122,3] 9.9e-3  6.2e-7 2.4e-5 9.9e-3 9.9e-3 9.9e-3 9.9e-310.0e-3   1543    1.0\n",
      "u_predict[123,3]10.0e-3  6.3e-7 2.5e-5 9.9e-310.0e-310.0e-310.0e-3   0.01   1544    1.0\n",
      "u_predict[124,3]   0.01  6.3e-7 2.5e-510.0e-3   0.01   0.01   0.01   0.01   1544    1.0\n",
      "u_predict[125,3]   0.01  6.3e-7 2.5e-5   0.01   0.01   0.01   0.01   0.01   1544    1.0\n",
      "u_predict[126,3]   0.01  6.4e-7 2.5e-5   0.01   0.01   0.01   0.01   0.01   1544    1.0\n",
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      "u_predict[397,3]   0.01  8.3e-7 3.2e-5   0.01   0.01   0.01   0.01   0.01   1492    1.0\n",
      "u_predict[398,3]   0.01  8.3e-7 3.2e-5   0.01   0.01   0.01   0.01   0.01   1492    1.0\n",
      "u_predict[399,3]   0.01  8.3e-7 3.2e-5   0.01   0.01   0.01   0.01   0.01   1492    1.0\n",
      "u_predict[400,3]   0.01  8.3e-7 3.2e-5   0.01   0.01   0.01   0.01   0.01   1492    1.0\n",
      "u_predict[401,3]   0.01  8.3e-7 3.2e-5   0.01   0.01   0.01   0.01   0.01   1492    1.0\n",
      "u_predict[402,3]   0.01  8.3e-7 3.2e-5   0.01   0.01   0.01   0.01   0.01   1492    1.0\n",
      "u_predict[403,3]   0.01  8.3e-7 3.2e-5   0.01   0.01   0.01   0.01   0.01   1492    1.0\n",
      "u_predict[404,3]   0.01  8.3e-7 3.2e-5   0.01   0.01   0.01   0.01   0.01   1492    1.0\n",
      "u_predict[405,3]   0.01  8.3e-7 3.2e-5   0.01   0.01   0.01   0.01   0.01   1492    1.0\n",
      "u_predict[406,3]   0.01  8.3e-7 3.2e-5   0.01   0.01   0.01   0.01   0.01   1492    1.0\n",
      "u_predict[407,3]   0.01  8.3e-7 3.2e-5   0.01   0.01   0.01   0.01   0.01   1492    1.0\n",
      "u_predict[408,3]   0.01  8.3e-7 3.2e-5   0.01   0.01   0.01   0.01   0.01   1492    1.0\n",
      "u_predict[409,3]   0.01  8.3e-7 3.2e-5   0.01   0.01   0.01   0.01   0.01   1492    1.0\n",
      "u_predict[410,3]   0.01  8.3e-7 3.2e-5   0.01   0.01   0.01   0.01   0.01   1492    1.0\n",
      "u_predict[411,3]   0.01  8.3e-7 3.2e-5   0.01   0.01   0.01   0.01   0.01   1492    1.0\n",
      "u_predict[412,3]   0.01  8.3e-7 3.2e-5   0.01   0.01   0.01   0.01   0.01   1492    1.0\n",
      "u_predict[413,3]   0.01  8.3e-7 3.2e-5   0.01   0.01   0.01   0.01   0.01   1492    1.0\n",
      "u_predict[1,4]   5.0e-3  1.1e-6 4.0e-5 4.9e-3 5.0e-3 5.0e-3 5.0e-3 5.1e-3   1392    1.0\n",
      "u_predict[2,4]   6.2e-3  1.2e-6 4.6e-5 6.1e-3 6.2e-3 6.2e-3 6.2e-3 6.3e-3   1404    1.0\n",
      "u_predict[3,4]   7.7e-3  1.4e-6 5.4e-5 7.6e-3 7.6e-3 7.7e-3 7.7e-3 7.8e-3   1419    1.0\n",
      "u_predict[4,4]   9.5e-3  1.6e-6 6.2e-5 9.4e-3 9.5e-3 9.5e-3 9.5e-3 9.6e-3   1437    1.0\n",
      "u_predict[5,4]     0.01  1.9e-6 7.1e-5   0.01   0.01   0.01   0.01   0.01   1461    1.0\n",
      "u_predict[6,4]     0.01  2.1e-6 8.2e-5   0.01   0.01   0.01   0.01   0.01   1490    1.0\n",
      "u_predict[7,4]     0.02  2.4e-6 9.4e-5   0.02   0.02   0.02   0.02   0.02   1525    1.0\n",
      "u_predict[8,4]     0.02  2.7e-6 1.1e-4   0.02   0.02   0.02   0.02   0.02   1567    1.0\n",
      "u_predict[9,4]     0.03  3.1e-6 1.2e-4   0.03   0.03   0.03   0.03   0.03   1610    1.0\n",
      "u_predict[10,4]    0.03  3.5e-6 1.4e-4   0.03   0.03   0.03   0.03   0.03   1640    1.0\n",
      "u_predict[11,4]    0.04  4.0e-6 1.6e-4   0.04   0.04   0.04   0.04   0.04   1669    1.0\n",
      "u_predict[12,4]    0.05  4.7e-6 1.9e-4   0.05   0.05   0.05   0.05   0.05   1689    1.0\n",
      "u_predict[13,4]    0.06  5.6e-6 2.3e-4   0.06   0.06   0.06   0.06   0.06   1697    1.0\n",
      "u_predict[14,4]    0.07  6.8e-6 2.8e-4   0.07   0.07   0.07   0.08   0.08   1690    1.0\n",
      "u_predict[15,4]    0.09  8.3e-6 3.4e-4   0.09   0.09   0.09   0.09   0.09   1669    1.0\n",
      "u_predict[16,4]    0.11  1.0e-5 4.2e-4   0.11   0.11   0.11   0.11   0.11   1641    1.0\n",
      "u_predict[17,4]    0.13  1.3e-5 5.1e-4   0.13   0.13   0.13   0.13   0.13   1609    1.0\n",
      "u_predict[18,4]    0.16  1.6e-5 6.3e-4   0.15   0.16   0.16   0.16   0.16   1578    1.0\n",
      "u_predict[19,4]    0.18  1.9e-5 7.6e-4   0.18   0.18   0.18   0.19   0.19   1550    1.0\n",
      "u_predict[20,4]    0.22  2.3e-5 9.1e-4   0.21   0.22   0.22   0.22   0.22   1496    1.0\n",
      "u_predict[21,4]    0.25  2.8e-5 1.1e-3   0.25   0.25   0.25   0.25   0.25   1448    1.0\n",
      "u_predict[22,4]    0.29  3.3e-5 1.2e-3   0.29   0.29   0.29   0.29   0.29   1408    1.0\n",
      "u_predict[23,4]    0.33  3.7e-5 1.4e-3   0.32   0.33   0.33   0.33   0.33   1375    1.0\n",
      "u_predict[24,4]    0.37  4.1e-5 1.5e-3   0.36   0.36   0.37   0.37   0.37   1348    1.0\n",
      "u_predict[25,4]     0.4  4.4e-5 1.6e-3    0.4    0.4    0.4   0.41   0.41   1326    1.0\n",
      "u_predict[26,4]    0.44  4.6e-5 1.6e-3   0.44   0.44   0.44   0.44   0.45   1307    1.0\n",
      "u_predict[27,4]    0.48  4.6e-5 1.7e-3   0.47   0.48   0.48   0.48   0.48   1292    1.0\n",
      "u_predict[28,4]    0.51  4.6e-5 1.6e-3   0.51   0.51   0.51   0.51   0.51   1280    1.0\n",
      "u_predict[29,4]    0.54  4.4e-5 1.6e-3   0.53   0.54   0.54   0.54   0.54   1270    1.0\n",
      "u_predict[30,4]    0.56  4.1e-5 1.5e-3   0.56   0.56   0.56   0.56   0.56   1264    1.0\n",
      "u_predict[31,4]    0.58  3.8e-5 1.4e-3   0.58   0.58   0.58   0.58   0.58   1260    1.0\n",
      "u_predict[32,4]    0.59  3.4e-5 1.2e-3   0.59   0.59   0.59   0.59    0.6   1259    1.0\n",
      "u_predict[33,4]     0.6  3.0e-5 1.1e-3    0.6    0.6    0.6    0.6   0.61   1263    1.0\n",
      "u_predict[34,4]    0.61  2.7e-5 9.5e-4   0.61   0.61   0.61   0.61   0.61   1269    1.0\n",
      "u_predict[35,4]    0.61  2.3e-5 8.2e-4   0.61   0.61   0.61   0.61   0.61   1273    1.0\n",
      "u_predict[36,4]    0.61  2.0e-5 7.0e-4   0.61   0.61   0.61   0.61   0.61   1283    1.0\n",
      "u_predict[37,4]    0.61  1.7e-5 6.1e-4   0.61   0.61   0.61   0.61   0.61   1299    1.0\n",
      "u_predict[38,4]     0.6  1.5e-5 5.3e-4    0.6    0.6    0.6    0.6    0.6   1323    1.0\n",
      "u_predict[39,4]     0.6  1.3e-5 4.8e-4    0.6    0.6    0.6    0.6    0.6   1352    1.0\n",
      "u_predict[40,4]    0.59  1.2e-5 4.4e-4   0.59   0.59   0.59   0.59   0.59   1369    1.0\n",
      "u_predict[41,4]    0.58  1.2e-5 4.3e-4   0.58   0.58   0.58   0.58   0.58   1376    1.0\n",
      "u_predict[42,4]    0.57  1.2e-5 4.3e-4   0.57   0.57   0.57   0.57   0.57   1384    1.0\n",
      "u_predict[43,4]    0.56  1.2e-5 4.4e-4   0.56   0.56   0.56   0.56   0.56   1391    1.0\n",
      "u_predict[44,4]    0.54  1.2e-5 4.5e-4   0.54   0.54   0.54   0.54   0.54   1397    1.0\n",
      "u_predict[45,4]    0.53  1.3e-5 4.7e-4   0.53   0.53   0.53   0.53   0.53   1397    1.0\n",
      "u_predict[46,4]    0.52  1.3e-5 4.9e-4   0.52   0.52   0.52   0.52   0.52   1394    1.0\n",
      "u_predict[47,4]    0.51  1.3e-5 5.0e-4   0.51   0.51   0.51   0.51   0.51   1392    1.0\n",
      "u_predict[48,4]    0.49  1.4e-5 5.2e-4   0.49   0.49   0.49   0.49   0.49   1390    1.0\n",
      "u_predict[49,4]    0.48  1.4e-5 5.3e-4   0.48   0.48   0.48   0.48   0.48   1388    1.0\n",
      "u_predict[50,4]    0.47  1.4e-5 5.4e-4   0.47   0.47   0.47   0.47   0.47   1387    1.0\n",
      "u_predict[51,4]    0.45  1.5e-5 5.5e-4   0.45   0.45   0.45   0.46   0.46   1387    1.0\n",
      "u_predict[52,4]    0.44  1.5e-5 5.6e-4   0.44   0.44   0.44   0.44   0.44   1387    1.0\n",
      "u_predict[53,4]    0.43  1.5e-5 5.6e-4   0.43   0.43   0.43   0.43   0.43   1387    1.0\n",
      "u_predict[54,4]    0.42  1.5e-5 5.7e-4   0.42   0.42   0.42   0.42   0.42   1387    1.0\n",
      "u_predict[55,4]    0.41  1.5e-5 5.7e-4    0.4   0.41   0.41   0.41   0.41   1388    1.0\n",
      "u_predict[56,4]    0.39  1.5e-5 5.7e-4   0.39   0.39   0.39   0.39    0.4   1389    1.0\n",
      "u_predict[57,4]    0.38  1.5e-5 5.8e-4   0.38   0.38   0.38   0.38   0.38   1391    1.0\n",
      "u_predict[58,4]    0.37  1.5e-5 5.8e-4   0.37   0.37   0.37   0.37   0.37   1392    1.0\n",
      "u_predict[59,4]    0.36  1.5e-5 5.8e-4   0.36   0.36   0.36   0.36   0.36   1394    1.0\n",
      "u_predict[60,4]    0.35  1.5e-5 5.8e-4   0.35   0.35   0.35   0.35   0.35   1396    1.0\n",
      "u_predict[61,4]    0.34  1.5e-5 5.7e-4   0.34   0.34   0.34   0.34   0.34   1397    1.0\n",
      "u_predict[62,4]    0.33  1.5e-5 5.7e-4   0.33   0.33   0.33   0.33   0.33   1399    1.0\n",
      "u_predict[63,4]    0.32  1.5e-5 5.7e-4   0.32   0.32   0.32   0.32   0.32   1401    1.0\n",
      "u_predict[64,4]    0.31  1.5e-5 5.7e-4   0.31   0.31   0.31   0.31   0.31   1403    1.0\n",
      "u_predict[65,4]     0.3  1.5e-5 5.6e-4    0.3    0.3    0.3    0.3    0.3   1406    1.0\n",
      "u_predict[66,4]    0.29  1.5e-5 5.6e-4   0.29   0.29   0.29   0.29   0.29   1408    1.0\n",
      "u_predict[67,4]    0.28  1.5e-5 5.5e-4   0.28   0.28   0.28   0.28   0.28   1410    1.0\n",
      "u_predict[68,4]    0.27  1.5e-5 5.5e-4   0.27   0.27   0.27   0.27   0.28   1411    1.0\n",
      "u_predict[69,4]    0.27  1.5e-5 5.5e-4   0.27   0.27   0.27   0.27   0.27   1411    1.0\n",
      "u_predict[70,4]    0.26  1.4e-5 5.4e-4   0.26   0.26   0.26   0.26   0.26   1411    1.0\n",
      "u_predict[71,4]    0.25  1.4e-5 5.4e-4   0.25   0.25   0.25   0.25   0.25   1411    1.0\n",
      "u_predict[72,4]    0.24  1.4e-5 5.3e-4   0.24   0.24   0.24   0.24   0.24   1411    1.0\n",
      "u_predict[73,4]    0.24  1.4e-5 5.3e-4   0.23   0.23   0.24   0.24   0.24   1411    1.0\n",
      "u_predict[74,4]    0.23  1.4e-5 5.2e-4   0.23   0.23   0.23   0.23   0.23   1411    1.0\n",
      "u_predict[75,4]    0.22  1.4e-5 5.1e-4   0.22   0.22   0.22   0.22   0.22   1412    1.0\n",
      "u_predict[76,4]    0.21  1.4e-5 5.1e-4   0.21   0.21   0.21   0.21   0.22   1412    1.0\n",
      "u_predict[77,4]    0.21  1.3e-5 5.0e-4   0.21   0.21   0.21   0.21   0.21   1412    1.0\n",
      "u_predict[78,4]     0.2  1.3e-5 5.0e-4    0.2    0.2    0.2    0.2    0.2   1412    1.0\n",
      "u_predict[79,4]     0.2  1.3e-5 4.9e-4   0.19   0.19    0.2    0.2    0.2   1412    1.0\n",
      "u_predict[80,4]    0.19  1.3e-5 4.8e-4   0.19   0.19   0.19   0.19   0.19   1412    1.0\n",
      "u_predict[81,4]    0.18  1.3e-5 4.8e-4   0.18   0.18   0.18   0.18   0.18   1412    1.0\n",
      "u_predict[82,4]    0.18  1.3e-5 4.7e-4   0.18   0.18   0.18   0.18   0.18   1413    1.0\n",
      "u_predict[83,4]    0.17  1.2e-5 4.7e-4   0.17   0.17   0.17   0.17   0.17   1413    1.0\n",
      "u_predict[84,4]    0.17  1.2e-5 4.6e-4   0.17   0.17   0.17   0.17   0.17   1413    1.0\n",
      "u_predict[85,4]    0.16  1.2e-5 4.5e-4   0.16   0.16   0.16   0.16   0.16   1413    1.0\n",
      "u_predict[86,4]    0.16  1.2e-5 4.5e-4   0.16   0.16   0.16   0.16   0.16   1413    1.0\n",
      "u_predict[87,4]    0.15  1.2e-5 4.4e-4   0.15   0.15   0.15   0.15   0.15   1413    1.0\n",
      "u_predict[88,4]    0.15  1.2e-5 4.3e-4   0.15   0.15   0.15   0.15   0.15   1413    1.0\n",
      "u_predict[89,4]    0.14  1.1e-5 4.3e-4   0.14   0.14   0.14   0.14   0.14   1414    1.0\n",
      "u_predict[90,4]    0.14  1.1e-5 4.2e-4   0.14   0.14   0.14   0.14   0.14   1414    1.0\n",
      "u_predict[91,4]    0.13  1.1e-5 4.1e-4   0.13   0.13   0.13   0.13   0.14   1414    1.0\n",
      "u_predict[92,4]    0.13  1.1e-5 4.1e-4   0.13   0.13   0.13   0.13   0.13   1414    1.0\n",
      "u_predict[93,4]    0.13  1.1e-5 4.0e-4   0.13   0.13   0.13   0.13   0.13   1414    1.0\n",
      "u_predict[94,4]    0.12  1.0e-5 3.9e-4   0.12   0.12   0.12   0.12   0.12   1414    1.0\n",
      "u_predict[95,4]    0.12  1.0e-5 3.9e-4   0.12   0.12   0.12   0.12   0.12   1414    1.0\n",
      "u_predict[96,4]    0.11  1.0e-5 3.8e-4   0.11   0.11   0.11   0.12   0.12   1415    1.0\n",
      "u_predict[97,4]    0.11 10.0e-6 3.8e-4   0.11   0.11   0.11   0.11   0.11   1415    1.0\n",
      "u_predict[98,4]    0.11  9.8e-6 3.7e-4   0.11   0.11   0.11   0.11   0.11   1415    1.0\n",
      "u_predict[99,4]     0.1  9.6e-6 3.6e-4    0.1    0.1    0.1    0.1   0.11   1415    1.0\n",
      "u_predict[100,4]    0.1  9.5e-6 3.6e-4    0.1    0.1    0.1    0.1    0.1   1415    1.0\n",
      "u_predict[101,4]    0.1  9.3e-6 3.5e-4    0.1    0.1    0.1    0.1    0.1   1415    1.0\n",
      "u_predict[102,4]    0.1  9.2e-6 3.4e-4   0.09   0.09    0.1    0.1    0.1   1415    1.0\n",
      "u_predict[103,4]   0.09  9.0e-6 3.4e-4   0.09   0.09   0.09   0.09   0.09   1415    1.0\n",
      "u_predict[104,4]   0.09  8.8e-6 3.3e-4   0.09   0.09   0.09   0.09   0.09   1415    1.0\n",
      "u_predict[105,4]   0.09  8.7e-6 3.3e-4   0.09   0.09   0.09   0.09   0.09   1416    1.0\n",
      "u_predict[106,4]   0.08  8.5e-6 3.2e-4   0.08   0.08   0.08   0.08   0.08   1416    1.0\n",
      "u_predict[107,4]   0.08  8.4e-6 3.1e-4   0.08   0.08   0.08   0.08   0.08   1416    1.0\n",
      "u_predict[108,4]   0.08  8.2e-6 3.1e-4   0.08   0.08   0.08   0.08   0.08   1416    1.0\n",
      "u_predict[109,4]   0.08  8.1e-6 3.0e-4   0.08   0.08   0.08   0.08   0.08   1416    1.0\n",
      "u_predict[110,4]   0.07  7.9e-6 3.0e-4   0.07   0.07   0.07   0.07   0.07   1416    1.0\n",
      "u_predict[111,4]   0.07  7.8e-6 2.9e-4   0.07   0.07   0.07   0.07   0.07   1416    1.0\n",
      "u_predict[112,4]   0.07  7.6e-6 2.9e-4   0.07   0.07   0.07   0.07   0.07   1416    1.0\n",
      "u_predict[113,4]   0.07  7.5e-6 2.8e-4   0.07   0.07   0.07   0.07   0.07   1416    1.0\n",
      "u_predict[114,4]   0.07  7.3e-6 2.8e-4   0.06   0.07   0.07   0.07   0.07   1416    1.0\n",
      "u_predict[115,4]   0.06  7.2e-6 2.7e-4   0.06   0.06   0.06   0.06   0.06   1417    1.0\n",
      "u_predict[116,4]   0.06  7.0e-6 2.7e-4   0.06   0.06   0.06   0.06   0.06   1417    1.0\n",
      "u_predict[117,4]   0.06  6.9e-6 2.6e-4   0.06   0.06   0.06   0.06   0.06   1417    1.0\n",
      "u_predict[118,4]   0.06  6.8e-6 2.5e-4   0.06   0.06   0.06   0.06   0.06   1417    1.0\n",
      "u_predict[119,4]   0.06  6.6e-6 2.5e-4   0.06   0.06   0.06   0.06   0.06   1417    1.0\n",
      "u_predict[120,4]   0.05  6.5e-6 2.4e-4   0.05   0.05   0.05   0.05   0.05   1417    1.0\n",
      "u_predict[121,4]   0.05  6.4e-6 2.4e-4   0.05   0.05   0.05   0.05   0.05   1417    1.0\n",
      "u_predict[122,4]   0.05  6.2e-6 2.3e-4   0.05   0.05   0.05   0.05   0.05   1417    1.0\n",
      "u_predict[123,4]   0.05  6.1e-6 2.3e-4   0.05   0.05   0.05   0.05   0.05   1417    1.0\n",
      "u_predict[124,4]   0.05  6.0e-6 2.3e-4   0.05   0.05   0.05   0.05   0.05   1417    1.0\n",
      "u_predict[125,4]   0.05  5.9e-6 2.2e-4   0.05   0.05   0.05   0.05   0.05   1417    1.0\n",
      "u_predict[126,4]   0.04  5.7e-6 2.2e-4   0.04   0.04   0.04   0.04   0.05   1417    1.0\n",
      "u_predict[127,4]   0.04  5.6e-6 2.1e-4   0.04   0.04   0.04   0.04   0.04   1417    1.0\n",
      "u_predict[128,4]   0.04  5.5e-6 2.1e-4   0.04   0.04   0.04   0.04   0.04   1418    1.0\n",
      "u_predict[129,4]   0.04  5.4e-6 2.0e-4   0.04   0.04   0.04   0.04   0.04   1418    1.0\n",
      "u_predict[130,4]   0.04  5.3e-6 2.0e-4   0.04   0.04   0.04   0.04   0.04   1418    1.0\n",
      "u_predict[131,4]   0.04  5.2e-6 1.9e-4   0.04   0.04   0.04   0.04   0.04   1418    1.0\n",
      "u_predict[132,4]   0.04  5.1e-6 1.9e-4   0.04   0.04   0.04   0.04   0.04   1418    1.0\n",
      "u_predict[133,4]   0.04  5.0e-6 1.9e-4   0.04   0.04   0.04   0.04   0.04   1418    1.0\n",
      "u_predict[134,4]   0.03  4.8e-6 1.8e-4   0.03   0.03   0.03   0.03   0.04   1418    1.0\n",
      "u_predict[135,4]   0.03  4.7e-6 1.8e-4   0.03   0.03   0.03   0.03   0.03   1418    1.0\n",
      "u_predict[136,4]   0.03  4.6e-6 1.7e-4   0.03   0.03   0.03   0.03   0.03   1418    1.0\n",
      "u_predict[137,4]   0.03  4.5e-6 1.7e-4   0.03   0.03   0.03   0.03   0.03   1418    1.0\n",
      "u_predict[138,4]   0.03  4.4e-6 1.7e-4   0.03   0.03   0.03   0.03   0.03   1418    1.0\n",
      "u_predict[139,4]   0.03  4.3e-6 1.6e-4   0.03   0.03   0.03   0.03   0.03   1418    1.0\n",
      "u_predict[140,4]   0.03  4.3e-6 1.6e-4   0.03   0.03   0.03   0.03   0.03   1418    1.0\n",
      "u_predict[141,4]   0.03  4.2e-6 1.6e-4   0.03   0.03   0.03   0.03   0.03   1418    1.0\n",
      "u_predict[142,4]   0.03  4.1e-6 1.5e-4   0.03   0.03   0.03   0.03   0.03   1418    1.0\n",
      "u_predict[143,4]   0.03  4.0e-6 1.5e-4   0.03   0.03   0.03   0.03   0.03   1418    1.0\n",
      "u_predict[144,4]   0.03  3.9e-6 1.5e-4   0.03   0.03   0.03   0.03   0.03   1418    1.0\n",
      "u_predict[145,4]   0.02  3.8e-6 1.4e-4   0.02   0.02   0.02   0.02   0.02   1419    1.0\n",
      "u_predict[146,4]   0.02  3.7e-6 1.4e-4   0.02   0.02   0.02   0.02   0.02   1419    1.0\n",
      "u_predict[147,4]   0.02  3.6e-6 1.4e-4   0.02   0.02   0.02   0.02   0.02   1419    1.0\n",
      "u_predict[148,4]   0.02  3.6e-6 1.3e-4   0.02   0.02   0.02   0.02   0.02   1419    1.0\n",
      "u_predict[149,4]   0.02  3.5e-6 1.3e-4   0.02   0.02   0.02   0.02   0.02   1419    1.0\n",
      "u_predict[150,4]   0.02  3.4e-6 1.3e-4   0.02   0.02   0.02   0.02   0.02   1419    1.0\n",
      "u_predict[151,4]   0.02  3.3e-6 1.2e-4   0.02   0.02   0.02   0.02   0.02   1419    1.0\n",
      "u_predict[152,4]   0.02  3.2e-6 1.2e-4   0.02   0.02   0.02   0.02   0.02   1419    1.0\n",
      "u_predict[153,4]   0.02  3.2e-6 1.2e-4   0.02   0.02   0.02   0.02   0.02   1419    1.0\n",
      "u_predict[154,4]   0.02  3.1e-6 1.2e-4   0.02   0.02   0.02   0.02   0.02   1419    1.0\n",
      "u_predict[155,4]   0.02  3.0e-6 1.1e-4   0.02   0.02   0.02   0.02   0.02   1419    1.0\n",
      "u_predict[156,4]   0.02  3.0e-6 1.1e-4   0.02   0.02   0.02   0.02   0.02   1419    1.0\n",
      "u_predict[157,4]   0.02  2.9e-6 1.1e-4   0.02   0.02   0.02   0.02   0.02   1419    1.0\n",
      "u_predict[158,4]   0.02  2.8e-6 1.1e-4   0.02   0.02   0.02   0.02   0.02   1419    1.0\n",
      "u_predict[159,4]   0.02  2.8e-6 1.0e-4   0.02   0.02   0.02   0.02   0.02   1419    1.0\n",
      "u_predict[160,4]   0.02  2.7e-6 1.0e-4   0.02   0.02   0.02   0.02   0.02   1419    1.0\n",
      "u_predict[161,4]   0.01  2.6e-6 9.9e-5   0.01   0.01   0.01   0.01   0.02   1419    1.0\n",
      "u_predict[162,4]   0.01  2.6e-6 9.7e-5   0.01   0.01   0.01   0.01   0.01   1419    1.0\n",
      "u_predict[163,4]   0.01  2.5e-6 9.4e-5   0.01   0.01   0.01   0.01   0.01   1419    1.0\n",
      "u_predict[164,4]   0.01  2.4e-6 9.2e-5   0.01   0.01   0.01   0.01   0.01   1419    1.0\n",
      "u_predict[165,4]   0.01  2.4e-6 9.0e-5   0.01   0.01   0.01   0.01   0.01   1419    1.0\n",
      "u_predict[166,4]   0.01  2.3e-6 8.8e-5   0.01   0.01   0.01   0.01   0.01   1419    1.0\n",
      "u_predict[167,4]   0.01  2.3e-6 8.6e-5   0.01   0.01   0.01   0.01   0.01   1419    1.0\n",
      "u_predict[168,4]   0.01  2.2e-6 8.4e-5   0.01   0.01   0.01   0.01   0.01   1420    1.0\n",
      "u_predict[169,4]   0.01  2.2e-6 8.2e-5   0.01   0.01   0.01   0.01   0.01   1420    1.0\n",
      "u_predict[170,4]   0.01  2.1e-6 8.0e-5   0.01   0.01   0.01   0.01   0.01   1420    1.0\n",
      "u_predict[171,4]   0.01  2.1e-6 7.8e-5   0.01   0.01   0.01   0.01   0.01   1420    1.0\n",
      "u_predict[172,4]   0.01  2.0e-6 7.6e-5   0.01   0.01   0.01   0.01   0.01   1420    1.0\n",
      "u_predict[173,4]   0.01  2.0e-6 7.4e-5   0.01   0.01   0.01   0.01   0.01   1420    1.0\n",
      "u_predict[174,4] 9.9e-3  1.9e-6 7.2e-5 9.8e-3 9.9e-3 9.9e-310.0e-3   0.01   1420    1.0\n",
      "u_predict[175,4] 9.6e-3  1.9e-6 7.1e-5 9.5e-3 9.5e-3 9.6e-3 9.6e-3 9.7e-3   1420    1.0\n",
      "u_predict[176,4] 9.3e-3  1.8e-6 6.9e-5 9.2e-3 9.2e-3 9.3e-3 9.3e-3 9.4e-3   1420    1.0\n",
      "u_predict[177,4] 9.0e-3  1.8e-6 6.7e-5 8.9e-3 9.0e-3 9.0e-3 9.1e-3 9.1e-3   1420    1.0\n",
      "u_predict[178,4] 8.7e-3  1.7e-6 6.6e-5 8.6e-3 8.7e-3 8.7e-3 8.8e-3 8.9e-3   1420    1.0\n",
      "u_predict[179,4] 8.5e-3  1.7e-6 6.4e-5 8.3e-3 8.4e-3 8.5e-3 8.5e-3 8.6e-3   1420    1.0\n",
      "u_predict[180,4] 8.2e-3  1.7e-6 6.3e-5 8.1e-3 8.2e-3 8.2e-3 8.2e-3 8.3e-3   1420    1.0\n",
      "u_predict[181,4] 7.9e-3  1.6e-6 6.1e-5 7.8e-3 7.9e-3 7.9e-3 8.0e-3 8.1e-3   1420    1.0\n",
      "u_predict[182,4] 7.7e-3  1.6e-6 6.0e-5 7.6e-3 7.7e-3 7.7e-3 7.7e-3 7.8e-3   1420    1.0\n",
      "u_predict[183,4] 7.5e-3  1.5e-6 5.8e-5 7.3e-3 7.4e-3 7.5e-3 7.5e-3 7.6e-3   1420    1.0\n",
      "u_predict[184,4] 7.2e-3  1.5e-6 5.7e-5 7.1e-3 7.2e-3 7.2e-3 7.3e-3 7.3e-3   1420    1.0\n",
      "u_predict[185,4] 7.0e-3  1.5e-6 5.5e-5 6.9e-3 7.0e-3 7.0e-3 7.0e-3 7.1e-3   1420    1.0\n",
      "u_predict[186,4] 6.8e-3  1.4e-6 5.4e-5 6.7e-3 6.8e-3 6.8e-3 6.8e-3 6.9e-3   1420    1.0\n",
      "u_predict[187,4] 6.6e-3  1.4e-6 5.3e-5 6.5e-3 6.5e-3 6.6e-3 6.6e-3 6.7e-3   1420    1.0\n",
      "u_predict[188,4] 6.4e-3  1.4e-6 5.1e-5 6.3e-3 6.3e-3 6.4e-3 6.4e-3 6.5e-3   1420    1.0\n",
      "u_predict[189,4] 6.2e-3  1.3e-6 5.0e-5 6.1e-3 6.1e-3 6.2e-3 6.2e-3 6.3e-3   1420    1.0\n",
      "u_predict[190,4] 6.0e-3  1.3e-6 4.9e-5 5.9e-3 6.0e-3 6.0e-3 6.0e-3 6.1e-3   1420    1.0\n",
      "u_predict[191,4] 5.8e-3  1.3e-6 4.8e-5 5.7e-3 5.8e-3 5.8e-3 5.8e-3 5.9e-3   1420    1.0\n",
      "u_predict[192,4] 5.6e-3  1.2e-6 4.6e-5 5.5e-3 5.6e-3 5.6e-3 5.7e-3 5.7e-3   1420    1.0\n",
      "u_predict[193,4] 5.4e-3  1.2e-6 4.5e-5 5.4e-3 5.4e-3 5.4e-3 5.5e-3 5.5e-3   1420    1.0\n",
      "u_predict[194,4] 5.3e-3  1.2e-6 4.4e-5 5.2e-3 5.2e-3 5.3e-3 5.3e-3 5.4e-3   1420    1.0\n",
      "u_predict[195,4] 5.1e-3  1.1e-6 4.3e-5 5.0e-3 5.1e-3 5.1e-3 5.1e-3 5.2e-3   1420    1.0\n",
      "u_predict[196,4] 5.0e-3  1.1e-6 4.2e-5 4.9e-3 4.9e-3 5.0e-3 5.0e-3 5.0e-3   1420    1.0\n",
      "u_predict[197,4] 4.8e-3  1.1e-6 4.1e-5 4.7e-3 4.8e-3 4.8e-3 4.8e-3 4.9e-3   1420    1.0\n",
      "u_predict[198,4] 4.7e-3  1.1e-6 4.0e-5 4.6e-3 4.6e-3 4.7e-3 4.7e-3 4.7e-3   1420    1.0\n",
      "u_predict[199,4] 4.5e-3  1.0e-6 3.9e-5 4.4e-3 4.5e-3 4.5e-3 4.5e-3 4.6e-3   1420    1.0\n",
      "u_predict[200,4] 4.4e-3  1.0e-6 3.8e-5 4.3e-3 4.3e-3 4.4e-3 4.4e-3 4.4e-3   1420    1.0\n",
      "u_predict[201,4] 4.2e-3  9.8e-7 3.7e-5 4.2e-3 4.2e-3 4.2e-3 4.3e-3 4.3e-3   1420    1.0\n",
      "u_predict[202,4] 4.1e-3  9.6e-7 3.6e-5 4.0e-3 4.1e-3 4.1e-3 4.1e-3 4.2e-3   1420    1.0\n",
      "u_predict[203,4] 4.0e-3  9.3e-7 3.5e-5 3.9e-3 4.0e-3 4.0e-3 4.0e-3 4.0e-3   1420    1.0\n",
      "u_predict[204,4] 3.9e-3  9.1e-7 3.4e-5 3.8e-3 3.8e-3 3.9e-3 3.9e-3 3.9e-3   1420    1.0\n",
      "u_predict[205,4] 3.7e-3  8.9e-7 3.3e-5 3.7e-3 3.7e-3 3.7e-3 3.8e-3 3.8e-3   1420    1.0\n",
      "u_predict[206,4] 3.6e-3  8.6e-7 3.3e-5 3.6e-3 3.6e-3 3.6e-3 3.6e-3 3.7e-3   1420    1.0\n",
      "u_predict[207,4] 3.5e-3  8.4e-7 3.2e-5 3.4e-3 3.5e-3 3.5e-3 3.5e-3 3.6e-3   1421    1.0\n",
      "u_predict[208,4] 3.4e-3  8.2e-7 3.1e-5 3.3e-3 3.4e-3 3.4e-3 3.4e-3 3.5e-3   1421    1.0\n",
      "u_predict[209,4] 3.3e-3  8.0e-7 3.0e-5 3.2e-3 3.3e-3 3.3e-3 3.3e-3 3.4e-3   1421    1.0\n",
      "u_predict[210,4] 3.2e-3  7.8e-7 2.9e-5 3.1e-3 3.2e-3 3.2e-3 3.2e-3 3.2e-3   1421    1.0\n",
      "u_predict[211,4] 3.1e-3  7.6e-7 2.9e-5 3.0e-3 3.1e-3 3.1e-3 3.1e-3 3.1e-3   1421    1.0\n",
      "u_predict[212,4] 3.0e-3  7.4e-7 2.8e-5 2.9e-3 3.0e-3 3.0e-3 3.0e-3 3.1e-3   1421    1.0\n",
      "u_predict[213,4] 2.9e-3  7.2e-7 2.7e-5 2.9e-3 2.9e-3 2.9e-3 2.9e-3 3.0e-3   1421    1.0\n",
      "u_predict[214,4] 2.8e-3  7.0e-7 2.7e-5 2.8e-3 2.8e-3 2.8e-3 2.8e-3 2.9e-3   1421    1.0\n",
      "u_predict[215,4] 2.7e-3  6.9e-7 2.6e-5 2.7e-3 2.7e-3 2.7e-3 2.7e-3 2.8e-3   1421    1.0\n",
      "u_predict[216,4] 2.6e-3  6.7e-7 2.5e-5 2.6e-3 2.6e-3 2.6e-3 2.7e-3 2.7e-3   1421    1.0\n",
      "u_predict[217,4] 2.6e-3  6.5e-7 2.5e-5 2.5e-3 2.5e-3 2.6e-3 2.6e-3 2.6e-3   1421    1.0\n",
      "u_predict[218,4] 2.5e-3  6.3e-7 2.4e-5 2.4e-3 2.5e-3 2.5e-3 2.5e-3 2.5e-3   1421    1.0\n",
      "u_predict[219,4] 2.4e-3  6.2e-7 2.3e-5 2.4e-3 2.4e-3 2.4e-3 2.4e-3 2.4e-3   1421    1.0\n",
      "u_predict[220,4] 2.3e-3  6.0e-7 2.3e-5 2.3e-3 2.3e-3 2.3e-3 2.3e-3 2.4e-3   1421    1.0\n",
      "u_predict[221,4] 2.3e-3  5.9e-7 2.2e-5 2.2e-3 2.2e-3 2.3e-3 2.3e-3 2.3e-3   1421    1.0\n",
      "u_predict[222,4] 2.2e-3  5.7e-7 2.2e-5 2.1e-3 2.2e-3 2.2e-3 2.2e-3 2.2e-3   1421    1.0\n",
      "u_predict[223,4] 2.1e-3  5.6e-7 2.1e-5 2.1e-3 2.1e-3 2.1e-3 2.1e-3 2.2e-3   1421    1.0\n",
      "u_predict[224,4] 2.1e-3  5.4e-7 2.0e-5 2.0e-3 2.0e-3 2.1e-3 2.1e-3 2.1e-3   1421    1.0\n",
      "u_predict[225,4] 2.0e-3  5.3e-7 2.0e-5 2.0e-3 2.0e-3 2.0e-3 2.0e-3 2.0e-3   1421    1.0\n",
      "u_predict[226,4] 1.9e-3  5.1e-7 1.9e-5 1.9e-3 1.9e-3 1.9e-3 1.9e-3 2.0e-3   1421    1.0\n",
      "u_predict[227,4] 1.9e-3  5.0e-7 1.9e-5 1.8e-3 1.9e-3 1.9e-3 1.9e-3 1.9e-3   1421    1.0\n",
      "u_predict[228,4] 1.8e-3  4.9e-7 1.8e-5 1.8e-3 1.8e-3 1.8e-3 1.8e-3 1.8e-3   1421    1.0\n",
      "u_predict[229,4] 1.8e-3  4.8e-7 1.8e-5 1.7e-3 1.7e-3 1.8e-3 1.8e-3 1.8e-3   1421    1.0\n",
      "u_predict[230,4] 1.7e-3  4.6e-7 1.7e-5 1.7e-3 1.7e-3 1.7e-3 1.7e-3 1.7e-3   1421    1.0\n",
      "u_predict[231,4] 1.6e-3  4.5e-7 1.7e-5 1.6e-3 1.6e-3 1.6e-3 1.7e-3 1.7e-3   1421    1.0\n",
      "u_predict[232,4] 1.6e-3  4.4e-7 1.7e-5 1.6e-3 1.6e-3 1.6e-3 1.6e-3 1.6e-3   1421    1.0\n",
      "u_predict[233,4] 1.5e-3  4.3e-7 1.6e-5 1.5e-3 1.5e-3 1.5e-3 1.6e-3 1.6e-3   1421    1.0\n",
      "u_predict[234,4] 1.5e-3  4.2e-7 1.6e-5 1.5e-3 1.5e-3 1.5e-3 1.5e-3 1.5e-3   1421    1.0\n",
      "u_predict[235,4] 1.5e-3  4.1e-7 1.5e-5 1.4e-3 1.4e-3 1.5e-3 1.5e-3 1.5e-3   1421    1.0\n",
      "u_predict[236,4] 1.4e-3  4.0e-7 1.5e-5 1.4e-3 1.4e-3 1.4e-3 1.4e-3 1.4e-3   1421    1.0\n",
      "u_predict[237,4] 1.4e-3  3.8e-7 1.5e-5 1.3e-3 1.4e-3 1.4e-3 1.4e-3 1.4e-3   1421    1.0\n",
      "u_predict[238,4] 1.3e-3  3.7e-7 1.4e-5 1.3e-3 1.3e-3 1.3e-3 1.3e-3 1.3e-3   1421    1.0\n",
      "u_predict[239,4] 1.3e-3  3.7e-7 1.4e-5 1.3e-3 1.3e-3 1.3e-3 1.3e-3 1.3e-3   1421    1.0\n",
      "u_predict[240,4] 1.2e-3  3.6e-7 1.3e-5 1.2e-3 1.2e-3 1.2e-3 1.3e-3 1.3e-3   1421    1.0\n",
      "u_predict[241,4] 1.2e-3  3.5e-7 1.3e-5 1.2e-3 1.2e-3 1.2e-3 1.2e-3 1.2e-3   1421    1.0\n",
      "u_predict[242,4] 1.2e-3  3.4e-7 1.3e-5 1.1e-3 1.2e-3 1.2e-3 1.2e-3 1.2e-3   1421    1.0\n",
      "u_predict[243,4] 1.1e-3  3.3e-7 1.2e-5 1.1e-3 1.1e-3 1.1e-3 1.1e-3 1.2e-3   1421    1.0\n",
      "u_predict[244,4] 1.1e-3  3.2e-7 1.2e-5 1.1e-3 1.1e-3 1.1e-3 1.1e-3 1.1e-3   1421    1.0\n",
      "u_predict[245,4] 1.1e-3  3.1e-7 1.2e-5 1.0e-3 1.1e-3 1.1e-3 1.1e-3 1.1e-3   1421    1.0\n",
      "u_predict[246,4] 1.0e-3  3.0e-7 1.1e-5 1.0e-3 1.0e-3 1.0e-3 1.0e-3 1.1e-3   1421    1.0\n",
      "u_predict[247,4]10.0e-4  2.9e-7 1.1e-5 9.8e-4 9.9e-410.0e-4 1.0e-3 1.0e-3   1421    1.0\n",
      "u_predict[248,4] 9.7e-4  2.9e-7 1.1e-5 9.4e-4 9.6e-4 9.6e-4 9.7e-4 9.9e-4   1421    1.0\n",
      "u_predict[249,4] 9.4e-4  2.8e-7 1.1e-5 9.2e-4 9.3e-4 9.3e-4 9.4e-4 9.6e-4   1421    1.0\n",
      "u_predict[250,4] 9.1e-4  2.7e-7 1.0e-5 8.9e-4 9.0e-4 9.1e-4 9.1e-4 9.3e-4   1421    1.0\n",
      "u_predict[251,4] 8.8e-4  2.6e-710.0e-6 8.6e-4 8.7e-4 8.8e-4 8.8e-4 9.0e-4   1421    1.0\n",
      "u_predict[252,4] 8.5e-4  2.6e-7 9.7e-6 8.3e-4 8.4e-4 8.5e-4 8.6e-4 8.7e-4   1421    1.0\n",
      "u_predict[253,4] 8.2e-4  2.5e-7 9.5e-6 8.1e-4 8.2e-4 8.2e-4 8.3e-4 8.4e-4   1421    1.0\n",
      "u_predict[254,4] 8.0e-4  2.4e-7 9.2e-6 7.8e-4 7.9e-4 8.0e-4 8.1e-4 8.2e-4   1421    1.0\n",
      "u_predict[255,4] 7.7e-4  2.4e-7 9.0e-6 7.6e-4 7.7e-4 7.7e-4 7.8e-4 7.9e-4   1421    1.0\n",
      "u_predict[256,4] 7.5e-4  2.3e-7 8.7e-6 7.3e-4 7.4e-4 7.5e-4 7.6e-4 7.7e-4   1421    1.0\n",
      "u_predict[257,4] 7.3e-4  2.3e-7 8.5e-6 7.1e-4 7.2e-4 7.3e-4 7.3e-4 7.4e-4   1421    1.0\n",
      "u_predict[258,4] 7.0e-4  2.2e-7 8.3e-6 6.9e-4 7.0e-4 7.0e-4 7.1e-4 7.2e-4   1421    1.0\n",
      "u_predict[259,4] 6.8e-4  2.1e-7 8.1e-6 6.7e-4 6.8e-4 6.8e-4 6.9e-4 7.0e-4   1421    1.0\n",
      "u_predict[260,4] 6.6e-4  2.1e-7 7.8e-6 6.5e-4 6.6e-4 6.6e-4 6.7e-4 6.8e-4   1422    1.0\n",
      "u_predict[261,4] 6.4e-4  2.0e-7 7.6e-6 6.3e-4 6.4e-4 6.4e-4 6.5e-4 6.6e-4   1422    1.0\n",
      "u_predict[262,4] 6.2e-4  2.0e-7 7.4e-6 6.1e-4 6.2e-4 6.2e-4 6.3e-4 6.4e-4   1422    1.0\n",
      "u_predict[263,4] 6.0e-4  1.9e-7 7.2e-6 5.9e-4 6.0e-4 6.0e-4 6.1e-4 6.2e-4   1422    1.0\n",
      "u_predict[264,4] 5.8e-4  1.9e-7 7.0e-6 5.7e-4 5.8e-4 5.8e-4 5.9e-4 6.0e-4   1422    1.0\n",
      "u_predict[265,4] 5.7e-4  1.8e-7 6.9e-6 5.5e-4 5.6e-4 5.6e-4 5.7e-4 5.8e-4   1422    1.0\n",
      "u_predict[266,4] 5.5e-4  1.8e-7 6.7e-6 5.4e-4 5.4e-4 5.5e-4 5.5e-4 5.6e-4   1422    1.0\n",
      "u_predict[267,4] 5.3e-4  1.7e-7 6.5e-6 5.2e-4 5.3e-4 5.3e-4 5.4e-4 5.4e-4   1422    1.0\n",
      "u_predict[268,4] 5.1e-4  1.7e-7 6.3e-6 5.0e-4 5.1e-4 5.1e-4 5.2e-4 5.3e-4   1422    1.0\n",
      "u_predict[269,4] 5.0e-4  1.6e-7 6.1e-6 4.9e-4 4.9e-4 5.0e-4 5.0e-4 5.1e-4   1422    1.0\n",
      "u_predict[270,4] 4.8e-4  1.6e-7 6.0e-6 4.7e-4 4.8e-4 4.8e-4 4.9e-4 4.9e-4   1422    1.0\n",
      "u_predict[271,4] 4.7e-4  1.5e-7 5.8e-6 4.6e-4 4.6e-4 4.7e-4 4.7e-4 4.8e-4   1422    1.0\n",
      "u_predict[272,4] 4.5e-4  1.5e-7 5.7e-6 4.4e-4 4.5e-4 4.5e-4 4.6e-4 4.6e-4   1421    1.0\n",
      "u_predict[273,4] 4.4e-4  1.5e-7 5.5e-6 4.3e-4 4.4e-4 4.4e-4 4.4e-4 4.5e-4   1421    1.0\n",
      "u_predict[274,4] 4.3e-4  1.4e-7 5.4e-6 4.2e-4 4.2e-4 4.3e-4 4.3e-4 4.4e-4   1421    1.0\n",
      "u_predict[275,4] 4.1e-4  1.4e-7 5.2e-6 4.0e-4 4.1e-4 4.1e-4 4.2e-4 4.2e-4   1421    1.0\n",
      "u_predict[276,4] 4.0e-4  1.3e-7 5.1e-6 3.9e-4 4.0e-4 4.0e-4 4.0e-4 4.1e-4   1421    1.0\n",
      "u_predict[277,4] 3.9e-4  1.3e-7 4.9e-6 3.8e-4 3.8e-4 3.9e-4 3.9e-4 4.0e-4   1421    1.0\n",
      "u_predict[278,4] 3.8e-4  1.3e-7 4.8e-6 3.7e-4 3.7e-4 3.8e-4 3.8e-4 3.8e-4   1421    1.0\n",
      "u_predict[279,4] 3.6e-4  1.2e-7 4.7e-6 3.6e-4 3.6e-4 3.6e-4 3.7e-4 3.7e-4   1421    1.0\n",
      "u_predict[280,4] 3.5e-4  1.2e-7 4.6e-6 3.4e-4 3.5e-4 3.5e-4 3.6e-4 3.6e-4   1421    1.0\n",
      "u_predict[281,4] 3.4e-4  1.2e-7 4.4e-6 3.3e-4 3.4e-4 3.4e-4 3.4e-4 3.5e-4   1421    1.0\n",
      "u_predict[282,4] 3.3e-4  1.1e-7 4.3e-6 3.2e-4 3.3e-4 3.3e-4 3.3e-4 3.4e-4   1421    1.0\n",
      "u_predict[283,4] 3.2e-4  1.1e-7 4.2e-6 3.1e-4 3.2e-4 3.2e-4 3.2e-4 3.3e-4   1422    1.0\n",
      "u_predict[284,4] 3.1e-4  1.1e-7 4.1e-6 3.0e-4 3.1e-4 3.1e-4 3.1e-4 3.2e-4   1422    1.0\n",
      "u_predict[285,4] 3.0e-4  1.1e-7 4.0e-6 2.9e-4 3.0e-4 3.0e-4 3.0e-4 3.1e-4   1422    1.0\n",
      "u_predict[286,4] 2.9e-4  1.0e-7 3.9e-6 2.8e-4 2.9e-4 2.9e-4 2.9e-4 3.0e-4   1422    1.0\n",
      "u_predict[287,4] 2.8e-4 10.0e-8 3.8e-6 2.8e-4 2.8e-4 2.8e-4 2.9e-4 2.9e-4   1422    1.0\n",
      "u_predict[288,4] 2.7e-4  9.7e-8 3.7e-6 2.7e-4 2.7e-4 2.7e-4 2.8e-4 2.8e-4   1422    1.0\n",
      "u_predict[289,4] 2.7e-4  9.4e-8 3.6e-6 2.6e-4 2.6e-4 2.7e-4 2.7e-4 2.7e-4   1422    1.0\n",
      "u_predict[290,4] 2.6e-4  9.2e-8 3.5e-6 2.5e-4 2.6e-4 2.6e-4 2.6e-4 2.6e-4   1422    1.0\n",
      "u_predict[291,4] 2.5e-4  8.9e-8 3.4e-6 2.4e-4 2.5e-4 2.5e-4 2.5e-4 2.6e-4   1422    1.0\n",
      "u_predict[292,4] 2.4e-4  8.7e-8 3.3e-6 2.4e-4 2.4e-4 2.4e-4 2.4e-4 2.5e-4   1422    1.0\n",
      "u_predict[293,4] 2.3e-4  8.5e-8 3.2e-6 2.3e-4 2.3e-4 2.3e-4 2.4e-4 2.4e-4   1422    1.0\n",
      "u_predict[294,4] 2.3e-4  8.2e-8 3.1e-6 2.2e-4 2.3e-4 2.3e-4 2.3e-4 2.3e-4   1422    1.0\n",
      "u_predict[295,4] 2.2e-4  8.0e-8 3.0e-6 2.1e-4 2.2e-4 2.2e-4 2.2e-4 2.3e-4   1422    1.0\n",
      "u_predict[296,4] 2.1e-4  7.8e-8 2.9e-6 2.1e-4 2.1e-4 2.1e-4 2.2e-4 2.2e-4   1422    1.0\n",
      "u_predict[297,4] 2.1e-4  7.6e-8 2.9e-6 2.0e-4 2.0e-4 2.1e-4 2.1e-4 2.1e-4   1422    1.0\n",
      "u_predict[298,4] 2.0e-4  7.4e-8 2.8e-6 2.0e-4 2.0e-4 2.0e-4 2.0e-4 2.1e-4   1422    1.0\n",
      "u_predict[299,4] 1.9e-4  7.1e-8 2.7e-6 1.9e-4 1.9e-4 1.9e-4 2.0e-4 2.0e-4   1422    1.0\n",
      "u_predict[300,4] 1.9e-4  6.9e-8 2.6e-6 1.8e-4 1.9e-4 1.9e-4 1.9e-4 1.9e-4   1422    1.0\n",
      "u_predict[301,4] 1.8e-4  6.8e-8 2.5e-6 1.8e-4 1.8e-4 1.8e-4 1.8e-4 1.9e-4   1422    1.0\n",
      "u_predict[302,4] 1.8e-4  6.6e-8 2.5e-6 1.7e-4 1.7e-4 1.8e-4 1.8e-4 1.8e-4   1421    1.0\n",
      "u_predict[303,4] 1.7e-4  6.4e-8 2.4e-6 1.7e-4 1.7e-4 1.7e-4 1.7e-4 1.8e-4   1421    1.0\n",
      "u_predict[304,4] 1.7e-4  6.2e-8 2.3e-6 1.6e-4 1.6e-4 1.7e-4 1.7e-4 1.7e-4   1421    1.0\n",
      "u_predict[305,4] 1.6e-4  6.0e-8 2.3e-6 1.6e-4 1.6e-4 1.6e-4 1.6e-4 1.6e-4   1421    1.0\n",
      "u_predict[306,4] 1.6e-4  5.9e-8 2.2e-6 1.5e-4 1.5e-4 1.6e-4 1.6e-4 1.6e-4   1421    1.0\n",
      "u_predict[307,4] 1.5e-4  5.7e-8 2.2e-6 1.5e-4 1.5e-4 1.5e-4 1.5e-4 1.5e-4   1421    1.0\n",
      "u_predict[308,4] 1.5e-4  5.6e-8 2.1e-6 1.4e-4 1.4e-4 1.5e-4 1.5e-4 1.5e-4   1421    1.0\n",
      "u_predict[309,4] 1.4e-4  5.4e-8 2.0e-6 1.4e-4 1.4e-4 1.4e-4 1.4e-4 1.5e-4   1421    1.0\n",
      "u_predict[310,4] 1.4e-4  5.3e-8 2.0e-6 1.3e-4 1.4e-4 1.4e-4 1.4e-4 1.4e-4   1421    1.0\n",
      "u_predict[311,4] 1.3e-4  5.1e-8 1.9e-6 1.3e-4 1.3e-4 1.3e-4 1.3e-4 1.4e-4   1421    1.0\n",
      "u_predict[312,4] 1.3e-4  5.0e-8 1.9e-6 1.3e-4 1.3e-4 1.3e-4 1.3e-4 1.3e-4   1422    1.0\n",
      "u_predict[313,4] 1.2e-4  4.9e-8 1.8e-6 1.2e-4 1.2e-4 1.2e-4 1.3e-4 1.3e-4   1422    1.0\n",
      "u_predict[314,4] 1.2e-4  4.7e-8 1.8e-6 1.2e-4 1.2e-4 1.2e-4 1.2e-4 1.2e-4   1422    1.0\n",
      "u_predict[315,4] 1.2e-4  4.6e-8 1.7e-6 1.1e-4 1.2e-4 1.2e-4 1.2e-4 1.2e-4   1422    1.0\n",
      "u_predict[316,4] 1.1e-4  4.5e-8 1.7e-6 1.1e-4 1.1e-4 1.1e-4 1.1e-4 1.2e-4   1422    1.0\n",
      "u_predict[317,4] 1.1e-4  4.4e-8 1.6e-6 1.1e-4 1.1e-4 1.1e-4 1.1e-4 1.1e-4   1422    1.0\n",
      "u_predict[318,4] 1.1e-4  4.2e-8 1.6e-6 1.0e-4 1.1e-4 1.1e-4 1.1e-4 1.1e-4   1423    1.0\n",
      "u_predict[319,4] 1.0e-4  4.1e-8 1.6e-6 1.0e-4 1.0e-4 1.0e-4 1.0e-4 1.1e-4   1423    1.0\n",
      "u_predict[320,4] 1.0e-4  4.0e-8 1.5e-6 9.8e-5 9.9e-5 1.0e-4 1.0e-4 1.0e-4   1423    1.0\n",
      "u_predict[321,4] 9.7e-5  3.9e-8 1.5e-6 9.4e-5 9.6e-5 9.7e-5 9.8e-5 1.0e-4   1423    1.0\n",
      "u_predict[322,4] 9.4e-5  3.8e-8 1.4e-6 9.2e-5 9.3e-5 9.4e-5 9.5e-5 9.7e-5   1423    1.0\n",
      "u_predict[323,4] 9.1e-5  3.7e-8 1.4e-6 8.9e-5 9.0e-5 9.1e-5 9.2e-5 9.4e-5   1423    1.0\n",
      "u_predict[324,4] 8.8e-5  3.6e-8 1.4e-6 8.6e-5 8.8e-5 8.8e-5 8.9e-5 9.1e-5   1423    1.0\n",
      "u_predict[325,4] 8.6e-5  3.5e-8 1.3e-6 8.3e-5 8.5e-5 8.6e-5 8.7e-5 8.8e-5   1423    1.0\n",
      "u_predict[326,4] 8.3e-5  3.4e-8 1.3e-6 8.1e-5 8.2e-5 8.3e-5 8.4e-5 8.6e-5   1423    1.0\n",
      "u_predict[327,4] 8.1e-5  3.3e-8 1.2e-6 7.8e-5 8.0e-5 8.0e-5 8.1e-5 8.3e-5   1423    1.0\n",
      "u_predict[328,4] 7.8e-5  3.2e-8 1.2e-6 7.6e-5 7.7e-5 7.8e-5 7.9e-5 8.0e-5   1423    1.0\n",
      "u_predict[329,4] 7.6e-5  3.1e-8 1.2e-6 7.3e-5 7.5e-5 7.6e-5 7.6e-5 7.8e-5   1423    1.0\n",
      "u_predict[330,4] 7.3e-5  3.0e-8 1.1e-6 7.1e-5 7.2e-5 7.3e-5 7.4e-5 7.5e-5   1422    1.0\n",
      "u_predict[331,4] 7.1e-5  2.9e-8 1.1e-6 6.9e-5 7.0e-5 7.1e-5 7.2e-5 7.3e-5   1422    1.0\n",
      "u_predict[332,4] 6.9e-5  2.8e-8 1.1e-6 6.7e-5 6.8e-5 6.9e-5 6.9e-5 7.1e-5   1422    1.0\n",
      "u_predict[333,4] 6.7e-5  2.8e-8 1.0e-6 6.5e-5 6.6e-5 6.7e-5 6.7e-5 6.9e-5   1422    1.0\n",
      "u_predict[334,4] 6.5e-5  2.7e-8 1.0e-6 6.3e-5 6.4e-5 6.4e-5 6.5e-5 6.6e-5   1421    1.0\n",
      "u_predict[335,4] 6.2e-5  2.6e-8 9.8e-7 6.1e-5 6.2e-5 6.2e-5 6.3e-5 6.4e-5   1421    1.0\n",
      "u_predict[336,4] 6.1e-5  2.5e-8 9.6e-7 5.9e-5 6.0e-5 6.0e-5 6.1e-5 6.2e-5   1421    1.0\n",
      "u_predict[337,4] 5.9e-5  2.5e-8 9.3e-7 5.7e-5 5.8e-5 5.9e-5 5.9e-5 6.0e-5   1421    1.0\n",
      "u_predict[338,4] 5.7e-5  2.4e-8 9.0e-7 5.5e-5 5.6e-5 5.7e-5 5.7e-5 5.9e-5   1421    1.0\n",
      "u_predict[339,4] 5.5e-5  2.3e-8 8.8e-7 5.3e-5 5.4e-5 5.5e-5 5.6e-5 5.7e-5   1420    1.0\n",
      "u_predict[340,4] 5.3e-5  2.3e-8 8.5e-7 5.2e-5 5.3e-5 5.3e-5 5.4e-5 5.5e-5   1420    1.0\n",
      "u_predict[341,4] 5.2e-5  2.2e-8 8.3e-7 5.0e-5 5.1e-5 5.2e-5 5.2e-5 5.3e-5   1420    1.0\n",
      "u_predict[342,4] 5.0e-5  2.1e-8 8.1e-7 4.9e-5 4.9e-5 5.0e-5 5.1e-5 5.2e-5   1420    1.0\n",
      "u_predict[343,4] 4.9e-5  2.1e-8 7.9e-7 4.7e-5 4.8e-5 4.8e-5 4.9e-5 5.0e-5   1420    1.0\n",
      "u_predict[344,4] 4.7e-5  2.0e-8 7.7e-7 4.6e-5 4.6e-5 4.7e-5 4.8e-5 4.9e-5   1421    1.0\n",
      "u_predict[345,4] 4.6e-5  2.0e-8 7.5e-7 4.4e-5 4.5e-5 4.5e-5 4.6e-5 4.7e-5   1421    1.0\n",
      "u_predict[346,4] 4.4e-5  1.9e-8 7.3e-7 4.3e-5 4.4e-5 4.4e-5 4.5e-5 4.6e-5   1421    1.0\n",
      "u_predict[347,4] 4.3e-5  1.9e-8 7.1e-7 4.1e-5 4.2e-5 4.3e-5 4.3e-5 4.4e-5   1421    1.0\n",
      "u_predict[348,4] 4.1e-5  1.8e-8 6.9e-7 4.0e-5 4.1e-5 4.1e-5 4.2e-5 4.3e-5   1422    1.0\n",
      "u_predict[349,4] 4.0e-5  1.8e-8 6.7e-7 3.9e-5 4.0e-5 4.0e-5 4.1e-5 4.2e-5   1422    1.0\n",
      "u_predict[350,4] 3.9e-5  1.7e-8 6.5e-7 3.8e-5 3.9e-5 3.9e-5 3.9e-5 4.0e-5   1422    1.0\n",
      "u_predict[351,4] 3.8e-5  1.7e-8 6.3e-7 3.7e-5 3.7e-5 3.8e-5 3.8e-5 3.9e-5   1423    1.0\n",
      "u_predict[352,4] 3.7e-5  1.6e-8 6.2e-7 3.5e-5 3.6e-5 3.7e-5 3.7e-5 3.8e-5   1423    1.0\n",
      "u_predict[353,4] 3.6e-5  1.6e-8 6.0e-7 3.4e-5 3.5e-5 3.5e-5 3.6e-5 3.7e-5   1423    1.0\n",
      "u_predict[354,4] 3.4e-5  1.6e-8 5.9e-7 3.3e-5 3.4e-5 3.4e-5 3.5e-5 3.6e-5   1424    1.0\n",
      "u_predict[355,4] 3.3e-5  1.5e-8 5.7e-7 3.2e-5 3.3e-5 3.3e-5 3.4e-5 3.4e-5   1424    1.0\n",
      "u_predict[356,4] 3.2e-5  1.5e-8 5.5e-7 3.1e-5 3.2e-5 3.2e-5 3.3e-5 3.3e-5   1424    1.0\n",
      "u_predict[357,4] 3.1e-5  1.4e-8 5.4e-7 3.0e-5 3.1e-5 3.1e-5 3.2e-5 3.2e-5   1425    1.0\n",
      "u_predict[358,4] 3.0e-5  1.4e-8 5.2e-7 2.9e-5 3.0e-5 3.0e-5 3.1e-5 3.1e-5   1425    1.0\n",
      "u_predict[359,4] 2.9e-5  1.3e-8 5.1e-7 2.9e-5 2.9e-5 2.9e-5 3.0e-5 3.0e-5   1425    1.0\n",
      "u_predict[360,4] 2.9e-5  1.3e-8 4.9e-7 2.8e-5 2.8e-5 2.9e-5 2.9e-5 3.0e-5   1425    1.0\n",
      "u_predict[361,4] 2.8e-5  1.3e-8 4.8e-7 2.7e-5 2.7e-5 2.8e-5 2.8e-5 2.9e-5   1425    1.0\n",
      "u_predict[362,4] 2.7e-5  1.2e-8 4.7e-7 2.6e-5 2.7e-5 2.7e-5 2.7e-5 2.8e-5   1425    1.0\n",
      "u_predict[363,4] 2.6e-5  1.2e-8 4.5e-7 2.5e-5 2.6e-5 2.6e-5 2.6e-5 2.7e-5   1425    1.0\n",
      "u_predict[364,4] 2.5e-5  1.2e-8 4.4e-7 2.4e-5 2.5e-5 2.5e-5 2.6e-5 2.6e-5   1425    1.0\n",
      "u_predict[365,4] 2.4e-5  1.1e-8 4.2e-7 2.4e-5 2.4e-5 2.4e-5 2.5e-5 2.5e-5   1425    1.0\n",
      "u_predict[366,4] 2.4e-5  1.1e-8 4.1e-7 2.3e-5 2.3e-5 2.4e-5 2.4e-5 2.5e-5   1424    1.0\n",
      "u_predict[367,4] 2.3e-5  1.1e-8 4.0e-7 2.2e-5 2.3e-5 2.3e-5 2.3e-5 2.4e-5   1424    1.0\n",
      "u_predict[368,4] 2.2e-5  1.0e-8 3.9e-7 2.2e-5 2.2e-5 2.2e-5 2.3e-5 2.3e-5   1424    1.0\n",
      "u_predict[369,4] 2.2e-5  9.9e-9 3.7e-7 2.1e-5 2.1e-5 2.2e-5 2.2e-5 2.2e-5   1423    1.0\n",
      "u_predict[370,4] 2.1e-5  9.6e-9 3.6e-7 2.0e-5 2.1e-5 2.1e-5 2.1e-5 2.2e-5   1422    1.0\n",
      "u_predict[371,4] 2.0e-5  9.3e-9 3.5e-7 2.0e-5 2.0e-5 2.0e-5 2.0e-5 2.1e-5   1422    1.0\n",
      "u_predict[372,4] 2.0e-5  9.1e-9 3.4e-7 1.9e-5 1.9e-5 2.0e-5 2.0e-5 2.0e-5   1421    1.0\n",
      "u_predict[373,4] 1.9e-5  8.8e-9 3.3e-7 1.8e-5 1.9e-5 1.9e-5 1.9e-5 2.0e-5   1420    1.0\n",
      "u_predict[374,4] 1.8e-5  8.5e-9 3.2e-7 1.8e-5 1.8e-5 1.8e-5 1.9e-5 1.9e-5   1420    1.0\n",
      "u_predict[375,4] 1.8e-5  8.3e-9 3.1e-7 1.7e-5 1.8e-5 1.8e-5 1.8e-5 1.8e-5   1419    1.0\n",
      "u_predict[376,4] 1.7e-5  8.1e-9 3.0e-7 1.7e-5 1.7e-5 1.7e-5 1.7e-5 1.8e-5   1419    1.0\n",
      "u_predict[377,4] 1.7e-5  7.8e-9 3.0e-7 1.6e-5 1.6e-5 1.7e-5 1.7e-5 1.7e-5   1418    1.0\n",
      "u_predict[378,4] 1.6e-5  7.6e-9 2.9e-7 1.6e-5 1.6e-5 1.6e-5 1.6e-5 1.7e-5   1418    1.0\n",
      "u_predict[379,4] 1.6e-5  7.4e-9 2.8e-7 1.5e-5 1.5e-5 1.6e-5 1.6e-5 1.6e-5   1417    1.0\n",
      "u_predict[380,4] 1.5e-5  7.2e-9 2.7e-7 1.5e-5 1.5e-5 1.5e-5 1.5e-5 1.6e-5   1417    1.0\n",
      "u_predict[381,4] 1.5e-5  7.0e-9 2.6e-7 1.4e-5 1.4e-5 1.5e-5 1.5e-5 1.5e-5   1417    1.0\n",
      "u_predict[382,4] 1.4e-5  6.8e-9 2.6e-7 1.4e-5 1.4e-5 1.4e-5 1.4e-5 1.5e-5   1417    1.0\n",
      "u_predict[383,4] 1.4e-5  6.7e-9 2.5e-7 1.3e-5 1.4e-5 1.4e-5 1.4e-5 1.4e-5   1417    1.0\n",
      "u_predict[384,4] 1.3e-5  6.5e-9 2.4e-7 1.3e-5 1.3e-5 1.3e-5 1.3e-5 1.4e-5   1416    1.0\n",
      "u_predict[385,4] 1.3e-5  6.3e-9 2.4e-7 1.2e-5 1.3e-5 1.3e-5 1.3e-5 1.3e-5   1416    1.0\n",
      "u_predict[386,4] 1.2e-5  6.2e-9 2.3e-7 1.2e-5 1.2e-5 1.2e-5 1.3e-5 1.3e-5   1416    1.0\n",
      "u_predict[387,4] 1.2e-5  6.0e-9 2.3e-7 1.2e-5 1.2e-5 1.2e-5 1.2e-5 1.3e-5   1416    1.0\n",
      "u_predict[388,4] 1.2e-5  5.9e-9 2.2e-7 1.1e-5 1.2e-5 1.2e-5 1.2e-5 1.2e-5   1416    1.0\n",
      "u_predict[389,4] 1.1e-5  5.7e-9 2.2e-7 1.1e-5 1.1e-5 1.1e-5 1.2e-5 1.2e-5   1417    1.0\n",
      "u_predict[390,4] 1.1e-5  5.6e-9 2.1e-7 1.1e-5 1.1e-5 1.1e-5 1.1e-5 1.1e-5   1417    1.0\n",
      "u_predict[391,4] 1.1e-5  5.4e-9 2.0e-7 1.0e-5 1.1e-5 1.1e-5 1.1e-5 1.1e-5   1417    1.0\n",
      "u_predict[392,4] 1.0e-5  5.3e-9 2.0e-7 1.0e-5 1.0e-5 1.0e-5 1.1e-5 1.1e-5   1417    1.0\n",
      "u_predict[393,4] 1.0e-5  5.2e-9 1.9e-7 9.7e-6 9.9e-6 1.0e-5 1.0e-5 1.0e-5   1418    1.0\n",
      "u_predict[394,4] 9.8e-6  5.0e-9 1.9e-7 9.4e-6 9.6e-6 9.8e-6 9.9e-6 1.0e-5   1418    1.0\n",
      "u_predict[395,4] 9.5e-6  4.9e-9 1.9e-7 9.1e-6 9.4e-6 9.5e-6 9.6e-6 9.8e-6   1419    1.0\n",
      "u_predict[396,4] 9.2e-6  4.8e-9 1.8e-7 8.9e-6 9.1e-6 9.2e-6 9.3e-6 9.6e-6   1419    1.0\n",
      "u_predict[397,4] 8.9e-6  4.7e-9 1.8e-7 8.6e-6 8.8e-6 8.9e-6 9.1e-6 9.3e-6   1420    1.0\n",
      "u_predict[398,4] 8.7e-6  4.5e-9 1.7e-7 8.4e-6 8.6e-6 8.7e-6 8.8e-6 9.0e-6   1421    1.0\n",
      "u_predict[399,4] 8.4e-6  4.4e-9 1.7e-7 8.1e-6 8.3e-6 8.4e-6 8.5e-6 8.7e-6   1421    1.0\n",
      "u_predict[400,4] 8.2e-6  4.3e-9 1.6e-7 7.9e-6 8.1e-6 8.2e-6 8.3e-6 8.5e-6   1422    1.0\n",
      "u_predict[401,4] 7.9e-6  4.2e-9 1.6e-7 7.6e-6 7.8e-6 7.9e-6 8.0e-6 8.2e-6   1423    1.0\n",
      "u_predict[402,4] 7.7e-6  4.0e-9 1.5e-7 7.4e-6 7.6e-6 7.7e-6 7.8e-6 8.0e-6   1424    1.0\n",
      "u_predict[403,4] 7.5e-6  3.9e-9 1.5e-7 7.2e-6 7.4e-6 7.5e-6 7.6e-6 7.8e-6   1425    1.0\n",
      "u_predict[404,4] 7.3e-6  3.8e-9 1.4e-7 7.0e-6 7.2e-6 7.2e-6 7.4e-6 7.5e-6   1425    1.0\n",
      "u_predict[405,4] 7.0e-6  3.7e-9 1.4e-7 6.8e-6 6.9e-6 7.0e-6 7.1e-6 7.3e-6   1426    1.0\n",
      "u_predict[406,4] 6.8e-6  3.6e-9 1.3e-7 6.6e-6 6.7e-6 6.8e-6 6.9e-6 7.1e-6   1427    1.0\n",
      "u_predict[407,4] 6.6e-6  3.4e-9 1.3e-7 6.4e-6 6.5e-6 6.6e-6 6.7e-6 6.9e-6   1427    1.0\n",
      "u_predict[408,4] 6.4e-6  3.3e-9 1.3e-7 6.2e-6 6.3e-6 6.4e-6 6.5e-6 6.7e-6   1427    1.0\n",
      "u_predict[409,4] 6.2e-6  3.2e-9 1.2e-7 6.0e-6 6.2e-6 6.2e-6 6.3e-6 6.5e-6   1427    1.0\n",
      "u_predict[410,4] 6.0e-6  3.1e-9 1.2e-7 5.8e-6 6.0e-6 6.0e-6 6.1e-6 6.3e-6   1428    1.0\n",
      "u_predict[411,4] 5.9e-6  3.0e-9 1.1e-7 5.6e-6 5.8e-6 5.9e-6 5.9e-6 6.1e-6   1428    1.0\n",
      "u_predict[412,4] 5.7e-6  2.9e-9 1.1e-7 5.5e-6 5.6e-6 5.7e-6 5.8e-6 5.9e-6   1428    1.0\n",
      "u_predict[413,4] 5.5e-6  2.8e-9 1.1e-7 5.3e-6 5.4e-6 5.5e-6 5.6e-6 5.7e-6   1428    1.0\n",
      "u_predict[1,5]      1.0  1.8e-7 6.7e-6    1.0    1.0    1.0    1.0    1.0   1452    1.0\n",
      "u_predict[2,5]      1.0  3.8e-7 1.5e-5    1.0    1.0    1.0    1.0    1.0   1458    1.0\n",
      "u_predict[3,5]      1.0  6.2e-7 2.4e-5    1.0    1.0    1.0    1.0    1.0   1472    1.0\n",
      "u_predict[4,5]     0.99  8.8e-7 3.4e-5   0.99   0.99   0.99   0.99   0.99   1490    1.0\n",
      "u_predict[5,5]     0.99  1.2e-6 4.6e-5   0.99   0.99   0.99   0.99   0.99   1514    1.0\n",
      "u_predict[6,5]     0.99  1.5e-6 5.9e-5   0.99   0.99   0.99   0.99   0.99   1542    1.0\n",
      "u_predict[7,5]     0.98  1.9e-6 7.4e-5   0.98   0.98   0.98   0.98   0.98   1575    1.0\n",
      "u_predict[8,5]     0.98  2.3e-6 9.1e-5   0.98   0.98   0.98   0.98   0.98   1613    1.0\n",
      "u_predict[9,5]     0.97  2.7e-6 1.1e-4   0.97   0.97   0.97   0.97   0.97   1639    1.0\n",
      "u_predict[10,5]    0.97  3.3e-6 1.3e-4   0.97   0.97   0.97   0.97   0.97   1664    1.0\n",
      "u_predict[11,5]    0.96  4.0e-6 1.6e-4   0.96   0.96   0.96   0.96   0.96   1685    1.0\n",
      "u_predict[12,5]    0.95  4.8e-6 2.0e-4   0.95   0.95   0.95   0.95   0.95   1699    1.0\n",
      "u_predict[13,5]    0.93  5.8e-6 2.4e-4   0.93   0.93   0.93   0.93   0.93   1703    1.0\n",
      "u_predict[14,5]    0.92  7.1e-6 2.9e-4   0.92   0.92   0.92   0.92   0.92   1695    1.0\n",
      "u_predict[15,5]     0.9  8.9e-6 3.6e-4    0.9    0.9    0.9    0.9    0.9   1675    1.0\n",
      "u_predict[16,5]    0.88  1.1e-5 4.5e-4   0.88   0.88   0.88   0.88   0.88   1648    1.0\n",
      "u_predict[17,5]    0.85  1.4e-5 5.5e-4   0.85   0.85   0.85   0.85   0.85   1618    1.0\n",
      "u_predict[18,5]    0.82  1.7e-5 6.8e-4   0.82   0.82   0.82   0.82   0.82   1589    1.0\n",
      "u_predict[19,5]    0.79  2.1e-5 8.3e-4   0.79   0.79   0.79   0.79   0.79   1553    1.0\n",
      "u_predict[20,5]    0.75  2.6e-510.0e-4   0.75   0.75   0.75   0.75   0.75   1496    1.0\n",
      "u_predict[21,5]    0.71  3.1e-5 1.2e-3   0.71   0.71   0.71   0.71   0.71   1448    1.0\n",
      "u_predict[22,5]    0.66  3.6e-5 1.4e-3   0.66   0.66   0.66   0.66   0.67   1408    1.0\n",
      "u_predict[23,5]    0.62  4.2e-5 1.5e-3   0.61   0.61   0.62   0.62   0.62   1374    1.0\n",
      "u_predict[24,5]    0.57  4.6e-5 1.7e-3   0.56   0.56   0.57   0.57   0.57   1345    1.0\n",
      "u_predict[25,5]    0.51  5.0e-5 1.8e-3   0.51   0.51   0.51   0.52   0.52   1321    1.0\n",
      "u_predict[26,5]    0.46  5.3e-5 1.9e-3   0.46   0.46   0.46   0.46   0.47   1300    1.0\n",
      "u_predict[27,5]    0.41  5.5e-5 2.0e-3   0.41   0.41   0.41   0.42   0.42   1283    1.0\n",
      "u_predict[28,5]    0.37  5.6e-5 2.0e-3   0.36   0.37   0.37   0.37   0.37   1267    1.0\n",
      "u_predict[29,5]    0.32  5.5e-5 2.0e-3   0.32   0.32   0.32   0.32   0.33   1254    1.0\n",
      "u_predict[30,5]    0.28  5.4e-5 1.9e-3   0.28   0.28   0.28   0.28   0.29   1242    1.0\n",
      "u_predict[31,5]    0.24  5.2e-5 1.8e-3   0.24   0.24   0.24   0.25   0.25   1231    1.0\n",
      "u_predict[32,5]    0.21  4.9e-5 1.7e-3   0.21   0.21   0.21   0.21   0.22   1221    1.0\n",
      "u_predict[33,5]    0.18  4.5e-5 1.6e-3   0.18   0.18   0.18   0.18   0.19   1212    1.0\n",
      "u_predict[34,5]    0.16  4.2e-5 1.4e-3   0.15   0.16   0.16   0.16   0.16   1204    1.0\n",
      "u_predict[35,5]    0.14  3.8e-5 1.3e-3   0.13   0.13   0.14   0.14   0.14   1196    1.0\n",
      "u_predict[36,5]    0.12  3.4e-5 1.2e-3   0.11   0.12   0.12   0.12   0.12   1189    1.0\n",
      "u_predict[37,5]     0.1  3.1e-5 1.1e-3    0.1    0.1    0.1    0.1    0.1   1184    1.0\n",
      "u_predict[38,5]    0.09  2.8e-5 9.6e-4   0.08   0.09   0.09   0.09   0.09   1181    1.0\n",
      "u_predict[39,5]    0.07  2.5e-5 8.6e-4   0.07   0.07   0.07   0.08   0.08   1179    1.0\n",
      "u_predict[40,5]    0.06  2.2e-5 7.7e-4   0.06   0.06   0.06   0.06   0.07   1177    1.0\n",
      "u_predict[41,5]    0.06  2.0e-5 6.9e-4   0.05   0.06   0.06   0.06   0.06   1175    1.0\n",
      "u_predict[42,5]    0.05  1.8e-5 6.1e-4   0.05   0.05   0.05   0.05   0.05   1173    1.0\n",
      "u_predict[43,5]    0.04  1.6e-5 5.5e-4   0.04   0.04   0.04   0.04   0.04   1171    1.0\n",
      "u_predict[44,5]    0.04  1.4e-5 4.9e-4   0.04   0.04   0.04   0.04   0.04   1169    1.0\n",
      "u_predict[45,5]    0.03  1.3e-5 4.4e-4   0.03   0.03   0.03   0.03   0.03   1167    1.0\n",
      "u_predict[46,5]    0.03  1.2e-5 4.0e-4   0.03   0.03   0.03   0.03   0.03   1166    1.0\n",
      "u_predict[47,5]    0.02  1.0e-5 3.6e-4   0.02   0.02   0.02   0.03   0.03   1164    1.0\n",
      "u_predict[48,5]    0.02  9.4e-6 3.2e-4   0.02   0.02   0.02   0.02   0.02   1162    1.0\n",
      "u_predict[49,5]    0.02  8.5e-6 2.9e-4   0.02   0.02   0.02   0.02   0.02   1160    1.0\n",
      "u_predict[50,5]    0.02  7.7e-6 2.6e-4   0.02   0.02   0.02   0.02   0.02   1159    1.0\n",
      "u_predict[51,5]    0.02  7.0e-6 2.4e-4   0.02   0.02   0.02   0.02   0.02   1157    1.0\n",
      "u_predict[52,5]    0.01  6.4e-6 2.2e-4   0.01   0.01   0.01   0.01   0.01   1155    1.0\n",
      "u_predict[53,5]    0.01  5.9e-6 2.0e-4   0.01   0.01   0.01   0.01   0.01   1153    1.0\n",
      "u_predict[54,5]    0.01  5.4e-6 1.8e-4   0.01   0.01   0.01   0.01   0.01   1152    1.0\n",
      "u_predict[55,5]    0.01  4.9e-6 1.7e-4 9.9e-3   0.01   0.01   0.01   0.01   1150    1.0\n",
      "u_predict[56,5]  9.2e-3  4.5e-6 1.5e-4 8.9e-3 9.1e-3 9.2e-3 9.3e-3 9.5e-3   1149    1.0\n",
      "u_predict[57,5]  8.4e-3  4.2e-6 1.4e-4 8.1e-3 8.3e-3 8.4e-3 8.5e-3 8.7e-3   1147    1.0\n",
      "u_predict[58,5]  7.6e-3  3.9e-6 1.3e-4 7.4e-3 7.5e-3 7.6e-3 7.7e-3 7.9e-3   1145    1.0\n",
      "u_predict[59,5]  7.0e-3  3.6e-6 1.2e-4 6.7e-3 6.9e-3 7.0e-3 7.1e-3 7.2e-3   1144    1.0\n",
      "u_predict[60,5]  6.4e-3  3.4e-6 1.1e-4 6.2e-3 6.3e-3 6.4e-3 6.5e-3 6.6e-3   1142    1.0\n",
      "u_predict[61,5]  5.9e-3  3.1e-6 1.1e-4 5.7e-3 5.8e-3 5.9e-3 5.9e-3 6.1e-3   1141    1.0\n",
      "u_predict[62,5]  5.4e-3  2.9e-6 9.9e-5 5.2e-3 5.3e-3 5.4e-3 5.5e-3 5.6e-3   1139    1.0\n",
      "u_predict[63,5]  5.0e-3  2.7e-6 9.2e-5 4.8e-3 4.9e-3 5.0e-3 5.0e-3 5.2e-3   1138    1.0\n",
      "u_predict[64,5]  4.6e-3  2.6e-6 8.7e-5 4.4e-3 4.6e-3 4.6e-3 4.7e-3 4.8e-3   1137    1.0\n",
      "u_predict[65,5]  4.3e-3  2.4e-6 8.1e-5 4.1e-3 4.2e-3 4.3e-3 4.3e-3 4.4e-3   1135    1.0\n",
      "u_predict[66,5]  4.0e-3  2.3e-6 7.7e-5 3.8e-3 3.9e-3 4.0e-3 4.0e-3 4.1e-3   1134    1.0\n",
      "u_predict[67,5]  3.7e-3  2.1e-6 7.2e-5 3.6e-3 3.7e-3 3.7e-3 3.8e-3 3.8e-3   1133    1.0\n",
      "u_predict[68,5]  3.5e-3  2.0e-6 6.8e-5 3.3e-3 3.4e-3 3.5e-3 3.5e-3 3.6e-3   1131    1.0\n",
      "u_predict[69,5]  3.2e-3  1.9e-6 6.5e-5 3.1e-3 3.2e-3 3.2e-3 3.3e-3 3.4e-3   1130    1.0\n",
      "u_predict[70,5]  3.0e-3  1.8e-6 6.1e-5 2.9e-3 3.0e-3 3.0e-3 3.1e-3 3.1e-3   1129    1.0\n",
      "u_predict[71,5]  2.8e-3  1.7e-6 5.8e-5 2.7e-3 2.8e-3 2.8e-3 2.9e-3 3.0e-3   1128    1.0\n",
      "u_predict[72,5]  2.7e-3  1.7e-6 5.6e-5 2.6e-3 2.6e-3 2.7e-3 2.7e-3 2.8e-3   1127    1.0\n",
      "u_predict[73,5]  2.5e-3  1.6e-6 5.3e-5 2.4e-3 2.5e-3 2.5e-3 2.6e-3 2.6e-3   1126    1.0\n",
      "u_predict[74,5]  2.4e-3  1.5e-6 5.1e-5 2.3e-3 2.4e-3 2.4e-3 2.4e-3 2.5e-3   1125    1.0\n",
      "u_predict[75,5]  2.3e-3  1.4e-6 4.8e-5 2.2e-3 2.2e-3 2.3e-3 2.3e-3 2.3e-3   1124    1.0\n",
      "u_predict[76,5]  2.1e-3  1.4e-6 4.6e-5 2.0e-3 2.1e-3 2.1e-3 2.2e-3 2.2e-3   1123    1.0\n",
      "u_predict[77,5]  2.0e-3  1.3e-6 4.5e-5 1.9e-3 2.0e-3 2.0e-3 2.1e-3 2.1e-3   1122    1.0\n",
      "u_predict[78,5]  1.9e-3  1.3e-6 4.3e-5 1.8e-3 1.9e-3 1.9e-3 2.0e-3 2.0e-3   1121    1.0\n",
      "u_predict[79,5]  1.8e-3  1.2e-6 4.1e-5 1.8e-3 1.8e-3 1.8e-3 1.9e-3 1.9e-3   1121    1.0\n",
      "u_predict[80,5]  1.8e-3  1.2e-6 4.0e-5 1.7e-3 1.7e-3 1.8e-3 1.8e-3 1.8e-3   1120    1.0\n",
      "u_predict[81,5]  1.7e-3  1.1e-6 3.8e-5 1.6e-3 1.6e-3 1.7e-3 1.7e-3 1.7e-3   1119    1.0\n",
      "u_predict[82,5]  1.6e-3  1.1e-6 3.7e-5 1.5e-3 1.6e-3 1.6e-3 1.6e-3 1.7e-3   1118    1.0\n",
      "u_predict[83,5]  1.5e-3  1.1e-6 3.6e-5 1.5e-3 1.5e-3 1.5e-3 1.6e-3 1.6e-3   1118    1.0\n",
      "u_predict[84,5]  1.5e-3  1.0e-6 3.5e-5 1.4e-3 1.4e-3 1.5e-3 1.5e-3 1.5e-3   1117    1.0\n",
      "u_predict[85,5]  1.4e-3  1.0e-6 3.4e-5 1.3e-3 1.4e-3 1.4e-3 1.4e-3 1.5e-3   1116    1.0\n",
      "u_predict[86,5]  1.4e-3  9.8e-7 3.3e-5 1.3e-3 1.3e-3 1.4e-3 1.4e-3 1.4e-3   1116    1.0\n",
      "u_predict[87,5]  1.3e-3  9.5e-7 3.2e-5 1.2e-3 1.3e-3 1.3e-3 1.3e-3 1.4e-3   1115    1.0\n",
      "u_predict[88,5]  1.3e-3  9.2e-7 3.1e-5 1.2e-3 1.2e-3 1.3e-3 1.3e-3 1.3e-3   1115    1.0\n",
      "u_predict[89,5]  1.2e-3  9.0e-7 3.0e-5 1.1e-3 1.2e-3 1.2e-3 1.2e-3 1.3e-3   1114    1.0\n",
      "u_predict[90,5]  1.2e-3  8.8e-7 2.9e-5 1.1e-3 1.2e-3 1.2e-3 1.2e-3 1.2e-3   1114    1.0\n",
      "u_predict[91,5]  1.1e-3  8.5e-7 2.8e-5 1.1e-3 1.1e-3 1.1e-3 1.2e-3 1.2e-3   1113    1.0\n",
      "u_predict[92,5]  1.1e-3  8.3e-7 2.8e-5 1.0e-3 1.1e-3 1.1e-3 1.1e-3 1.1e-3   1113    1.0\n",
      "u_predict[93,5]  1.1e-3  8.1e-7 2.7e-5 1.0e-3 1.0e-3 1.1e-3 1.1e-3 1.1e-3   1112    1.0\n",
      "u_predict[94,5]  1.0e-3  8.0e-7 2.7e-5 9.7e-4 1.0e-3 1.0e-3 1.0e-3 1.1e-3   1112    1.0\n",
      "u_predict[95,5] 10.0e-4  7.8e-7 2.6e-5 9.4e-4 9.8e-4 1.0e-3 1.0e-3 1.0e-3   1111    1.0\n",
      "u_predict[96,5]  9.7e-4  7.6e-7 2.5e-5 9.2e-4 9.6e-4 9.7e-4 9.9e-4 1.0e-3   1111    1.0\n",
      "u_predict[97,5]  9.4e-4  7.5e-7 2.5e-5 8.9e-4 9.3e-4 9.4e-4 9.6e-4 9.9e-4   1111    1.0\n",
      "u_predict[98,5]  9.2e-4  7.3e-7 2.4e-5 8.7e-4 9.0e-4 9.2e-4 9.3e-4 9.6e-4   1110    1.0\n",
      "u_predict[99,5]  8.9e-4  7.2e-7 2.4e-5 8.4e-4 8.8e-4 9.0e-4 9.1e-4 9.4e-4   1110    1.0\n",
      "u_predict[100,5] 8.7e-4  7.1e-7 2.4e-5 8.2e-4 8.6e-4 8.7e-4 8.9e-4 9.1e-4   1110    1.0\n",
      "u_predict[101,5] 8.5e-4  6.9e-7 2.3e-5 8.0e-4 8.4e-4 8.5e-4 8.7e-4 8.9e-4   1109    1.0\n",
      "u_predict[102,5] 8.3e-4  6.8e-7 2.3e-5 7.8e-4 8.2e-4 8.3e-4 8.4e-4 8.7e-4   1109    1.0\n",
      "u_predict[103,5] 8.1e-4  6.7e-7 2.2e-5 7.6e-4 8.0e-4 8.1e-4 8.3e-4 8.5e-4   1109    1.0\n",
      "u_predict[104,5] 7.9e-4  6.6e-7 2.2e-5 7.5e-4 7.8e-4 7.9e-4 8.1e-4 8.3e-4   1108    1.0\n",
      "u_predict[105,5] 7.8e-4  6.5e-7 2.2e-5 7.3e-4 7.6e-4 7.8e-4 7.9e-4 8.1e-4   1108    1.0\n",
      "u_predict[106,5] 7.6e-4  6.4e-7 2.1e-5 7.1e-4 7.5e-4 7.6e-4 7.7e-4 8.0e-4   1108    1.0\n",
      "u_predict[107,5] 7.4e-4  6.3e-7 2.1e-5 7.0e-4 7.3e-4 7.5e-4 7.6e-4 7.8e-4   1108    1.0\n",
      "u_predict[108,5] 7.3e-4  6.2e-7 2.1e-5 6.8e-4 7.2e-4 7.3e-4 7.4e-4 7.7e-4   1108    1.0\n",
      "u_predict[109,5] 7.2e-4  6.1e-7 2.0e-5 6.7e-4 7.0e-4 7.2e-4 7.3e-4 7.5e-4   1107    1.0\n",
      "u_predict[110,5] 7.0e-4  6.1e-7 2.0e-5 6.6e-4 6.9e-4 7.0e-4 7.2e-4 7.4e-4   1107    1.0\n",
      "u_predict[111,5] 6.9e-4  6.0e-7 2.0e-5 6.5e-4 6.8e-4 6.9e-4 7.0e-4 7.2e-4   1107    1.0\n",
      "u_predict[112,5] 6.8e-4  5.9e-7 2.0e-5 6.4e-4 6.7e-4 6.8e-4 6.9e-4 7.1e-4   1107    1.0\n",
      "u_predict[113,5] 6.7e-4  5.8e-7 1.9e-5 6.2e-4 6.6e-4 6.7e-4 6.8e-4 7.0e-4   1107    1.0\n",
      "u_predict[114,5] 6.6e-4  5.8e-7 1.9e-5 6.1e-4 6.4e-4 6.6e-4 6.7e-4 6.9e-4   1106    1.0\n",
      "u_predict[115,5] 6.5e-4  5.7e-7 1.9e-5 6.0e-4 6.3e-4 6.5e-4 6.6e-4 6.8e-4   1106    1.0\n",
      "u_predict[116,5] 6.4e-4  5.7e-7 1.9e-5 5.9e-4 6.2e-4 6.4e-4 6.5e-4 6.7e-4   1105    1.0\n",
      "u_predict[117,5] 6.3e-4  5.6e-7 1.9e-5 5.9e-4 6.2e-4 6.3e-4 6.4e-4 6.6e-4   1105    1.0\n",
      "u_predict[118,5] 6.2e-4  5.5e-7 1.8e-5 5.8e-4 6.1e-4 6.2e-4 6.3e-4 6.5e-4   1105    1.0\n",
      "u_predict[119,5] 6.1e-4  5.5e-7 1.8e-5 5.7e-4 6.0e-4 6.1e-4 6.2e-4 6.4e-4   1104    1.0\n",
      "u_predict[120,5] 6.0e-4  5.4e-7 1.8e-5 5.6e-4 5.9e-4 6.0e-4 6.1e-4 6.3e-4   1104    1.0\n",
      "u_predict[121,5] 5.9e-4  5.4e-7 1.8e-5 5.5e-4 5.8e-4 5.9e-4 6.0e-4 6.2e-4   1104    1.0\n",
      "u_predict[122,5] 5.8e-4  5.4e-7 1.8e-5 5.5e-4 5.7e-4 5.9e-4 6.0e-4 6.1e-4   1103    1.0\n",
      "u_predict[123,5] 5.8e-4  5.3e-7 1.8e-5 5.4e-4 5.7e-4 5.8e-4 5.9e-4 6.1e-4   1103    1.0\n",
      "u_predict[124,5] 5.7e-4  5.3e-7 1.7e-5 5.3e-4 5.6e-4 5.7e-4 5.8e-4 6.0e-4   1103    1.0\n",
      "u_predict[125,5] 5.6e-4  5.2e-7 1.7e-5 5.3e-4 5.5e-4 5.7e-4 5.7e-4 5.9e-4   1103    1.0\n",
      "u_predict[126,5] 5.6e-4  5.2e-7 1.7e-5 5.2e-4 5.5e-4 5.6e-4 5.7e-4 5.9e-4   1102    1.0\n",
      "u_predict[127,5] 5.5e-4  5.2e-7 1.7e-5 5.1e-4 5.4e-4 5.5e-4 5.6e-4 5.8e-4   1102    1.0\n",
      "u_predict[128,5] 5.5e-4  5.1e-7 1.7e-5 5.1e-4 5.4e-4 5.5e-4 5.6e-4 5.7e-4   1102    1.0\n",
      "u_predict[129,5] 5.4e-4  5.1e-7 1.7e-5 5.0e-4 5.3e-4 5.4e-4 5.5e-4 5.7e-4   1102    1.0\n",
      "u_predict[130,5] 5.3e-4  5.1e-7 1.7e-5 5.0e-4 5.3e-4 5.4e-4 5.5e-4 5.6e-4   1101    1.0\n",
      "u_predict[131,5] 5.3e-4  5.0e-7 1.7e-5 4.9e-4 5.2e-4 5.3e-4 5.4e-4 5.6e-4   1101    1.0\n",
      "u_predict[132,5] 5.2e-4  5.0e-7 1.7e-5 4.9e-4 5.2e-4 5.3e-4 5.3e-4 5.5e-4   1101    1.0\n",
      "u_predict[133,5] 5.2e-4  5.0e-7 1.6e-5 4.8e-4 5.1e-4 5.2e-4 5.3e-4 5.5e-4   1101    1.0\n",
      "u_predict[134,5] 5.1e-4  4.9e-7 1.6e-5 4.8e-4 5.1e-4 5.2e-4 5.3e-4 5.4e-4   1100    1.0\n",
      "u_predict[135,5] 5.1e-4  4.9e-7 1.6e-5 4.7e-4 5.0e-4 5.1e-4 5.2e-4 5.4e-4   1100    1.0\n",
      "u_predict[136,5] 5.1e-4  4.9e-7 1.6e-5 4.7e-4 5.0e-4 5.1e-4 5.2e-4 5.3e-4   1100    1.0\n",
      "u_predict[137,5] 5.0e-4  4.9e-7 1.6e-5 4.7e-4 4.9e-4 5.0e-4 5.1e-4 5.3e-4   1100    1.0\n",
      "u_predict[138,5] 5.0e-4  4.8e-7 1.6e-5 4.6e-4 4.9e-4 5.0e-4 5.1e-4 5.3e-4   1100    1.0\n",
      "u_predict[139,5] 4.9e-4  4.8e-7 1.6e-5 4.6e-4 4.9e-4 5.0e-4 5.0e-4 5.2e-4   1099    1.0\n",
      "u_predict[140,5] 4.9e-4  4.8e-7 1.6e-5 4.6e-4 4.8e-4 4.9e-4 5.0e-4 5.2e-4   1099    1.0\n",
      "u_predict[141,5] 4.9e-4  4.8e-7 1.6e-5 4.5e-4 4.8e-4 4.9e-4 5.0e-4 5.1e-4   1099    1.0\n",
      "u_predict[142,5] 4.8e-4  4.8e-7 1.6e-5 4.5e-4 4.8e-4 4.9e-4 4.9e-4 5.1e-4   1099    1.0\n",
      "u_predict[143,5] 4.8e-4  4.7e-7 1.6e-5 4.5e-4 4.7e-4 4.8e-4 4.9e-4 5.1e-4   1099    1.0\n",
      "u_predict[144,5] 4.8e-4  4.7e-7 1.6e-5 4.4e-4 4.7e-4 4.8e-4 4.9e-4 5.0e-4   1099    1.0\n",
      "u_predict[145,5] 4.8e-4  4.7e-7 1.6e-5 4.4e-4 4.7e-4 4.8e-4 4.8e-4 5.0e-4   1099    1.0\n",
      "u_predict[146,5] 4.7e-4  4.7e-7 1.6e-5 4.4e-4 4.6e-4 4.7e-4 4.8e-4 5.0e-4   1098    1.0\n",
      "u_predict[147,5] 4.7e-4  4.7e-7 1.5e-5 4.4e-4 4.6e-4 4.7e-4 4.8e-4 5.0e-4   1098    1.0\n",
      "u_predict[148,5] 4.7e-4  4.6e-7 1.5e-5 4.3e-4 4.6e-4 4.7e-4 4.8e-4 4.9e-4   1098    1.0\n",
      "u_predict[149,5] 4.6e-4  4.6e-7 1.5e-5 4.3e-4 4.6e-4 4.7e-4 4.7e-4 4.9e-4   1098    1.0\n",
      "u_predict[150,5] 4.6e-4  4.6e-7 1.5e-5 4.3e-4 4.5e-4 4.6e-4 4.7e-4 4.9e-4   1098    1.0\n",
      "u_predict[151,5] 4.6e-4  4.6e-7 1.5e-5 4.3e-4 4.5e-4 4.6e-4 4.7e-4 4.8e-4   1098    1.0\n",
      "u_predict[152,5] 4.6e-4  4.6e-7 1.5e-5 4.2e-4 4.5e-4 4.6e-4 4.7e-4 4.8e-4   1098    1.0\n",
      "u_predict[153,5] 4.5e-4  4.6e-7 1.5e-5 4.2e-4 4.5e-4 4.6e-4 4.6e-4 4.8e-4   1098    1.0\n",
      "u_predict[154,5] 4.5e-4  4.6e-7 1.5e-5 4.2e-4 4.4e-4 4.5e-4 4.6e-4 4.8e-4   1097    1.0\n",
      "u_predict[155,5] 4.5e-4  4.6e-7 1.5e-5 4.2e-4 4.4e-4 4.5e-4 4.6e-4 4.8e-4   1097    1.0\n",
      "u_predict[156,5] 4.5e-4  4.5e-7 1.5e-5 4.1e-4 4.4e-4 4.5e-4 4.6e-4 4.7e-4   1097    1.0\n",
      "u_predict[157,5] 4.5e-4  4.5e-7 1.5e-5 4.1e-4 4.4e-4 4.5e-4 4.6e-4 4.7e-4   1097    1.0\n",
      "u_predict[158,5] 4.4e-4  4.5e-7 1.5e-5 4.1e-4 4.4e-4 4.5e-4 4.5e-4 4.7e-4   1097    1.0\n",
      "u_predict[159,5] 4.4e-4  4.5e-7 1.5e-5 4.1e-4 4.4e-4 4.4e-4 4.5e-4 4.7e-4   1097    1.0\n",
      "u_predict[160,5] 4.4e-4  4.5e-7 1.5e-5 4.1e-4 4.3e-4 4.4e-4 4.5e-4 4.7e-4   1097    1.0\n",
      "u_predict[161,5] 4.4e-4  4.5e-7 1.5e-5 4.1e-4 4.3e-4 4.4e-4 4.5e-4 4.6e-4   1097    1.0\n",
      "u_predict[162,5] 4.4e-4  4.5e-7 1.5e-5 4.0e-4 4.3e-4 4.4e-4 4.5e-4 4.6e-4   1097    1.0\n",
      "u_predict[163,5] 4.4e-4  4.5e-7 1.5e-5 4.0e-4 4.3e-4 4.4e-4 4.5e-4 4.6e-4   1097    1.0\n",
      "u_predict[164,5] 4.4e-4  4.5e-7 1.5e-5 4.0e-4 4.3e-4 4.4e-4 4.4e-4 4.6e-4   1097    1.0\n",
      "u_predict[165,5] 4.3e-4  4.5e-7 1.5e-5 4.0e-4 4.3e-4 4.3e-4 4.4e-4 4.6e-4   1096    1.0\n",
      "u_predict[166,5] 4.3e-4  4.5e-7 1.5e-5 4.0e-4 4.2e-4 4.3e-4 4.4e-4 4.6e-4   1096    1.0\n",
      "u_predict[167,5] 4.3e-4  4.4e-7 1.5e-5 4.0e-4 4.2e-4 4.3e-4 4.4e-4 4.6e-4   1096    1.0\n",
      "u_predict[168,5] 4.3e-4  4.4e-7 1.5e-5 4.0e-4 4.2e-4 4.3e-4 4.4e-4 4.5e-4   1096    1.0\n",
      "u_predict[169,5] 4.3e-4  4.4e-7 1.5e-5 4.0e-4 4.2e-4 4.3e-4 4.4e-4 4.5e-4   1096    1.0\n",
      "u_predict[170,5] 4.3e-4  4.4e-7 1.5e-5 3.9e-4 4.2e-4 4.3e-4 4.4e-4 4.5e-4   1096    1.0\n",
      "u_predict[171,5] 4.3e-4  4.4e-7 1.5e-5 3.9e-4 4.2e-4 4.3e-4 4.4e-4 4.5e-4   1096    1.0\n",
      "u_predict[172,5] 4.2e-4  4.4e-7 1.5e-5 3.9e-4 4.2e-4 4.3e-4 4.3e-4 4.5e-4   1096    1.0\n",
      "u_predict[173,5] 4.2e-4  4.4e-7 1.5e-5 3.9e-4 4.2e-4 4.3e-4 4.3e-4 4.5e-4   1096    1.0\n",
      "u_predict[174,5] 4.2e-4  4.4e-7 1.5e-5 3.9e-4 4.1e-4 4.2e-4 4.3e-4 4.5e-4   1096    1.0\n",
      "u_predict[175,5] 4.2e-4  4.4e-7 1.5e-5 3.9e-4 4.1e-4 4.2e-4 4.3e-4 4.5e-4   1096    1.0\n",
      "u_predict[176,5] 4.2e-4  4.4e-7 1.5e-5 3.9e-4 4.1e-4 4.2e-4 4.3e-4 4.4e-4   1096    1.0\n",
      "u_predict[177,5] 4.2e-4  4.4e-7 1.5e-5 3.9e-4 4.1e-4 4.2e-4 4.3e-4 4.4e-4   1096    1.0\n",
      "u_predict[178,5] 4.2e-4  4.4e-7 1.4e-5 3.9e-4 4.1e-4 4.2e-4 4.3e-4 4.4e-4   1096    1.0\n",
      "u_predict[179,5] 4.2e-4  4.4e-7 1.4e-5 3.8e-4 4.1e-4 4.2e-4 4.3e-4 4.4e-4   1096    1.0\n",
      "u_predict[180,5] 4.2e-4  4.4e-7 1.4e-5 3.8e-4 4.1e-4 4.2e-4 4.3e-4 4.4e-4   1095    1.0\n",
      "u_predict[181,5] 4.2e-4  4.4e-7 1.4e-5 3.8e-4 4.1e-4 4.2e-4 4.3e-4 4.4e-4   1095    1.0\n",
      "u_predict[182,5] 4.2e-4  4.4e-7 1.4e-5 3.8e-4 4.1e-4 4.2e-4 4.2e-4 4.4e-4   1095    1.0\n",
      "u_predict[183,5] 4.1e-4  4.4e-7 1.4e-5 3.8e-4 4.1e-4 4.2e-4 4.2e-4 4.4e-4   1095    1.0\n",
      "u_predict[184,5] 4.1e-4  4.4e-7 1.4e-5 3.8e-4 4.1e-4 4.2e-4 4.2e-4 4.4e-4   1095    1.0\n",
      "u_predict[185,5] 4.1e-4  4.4e-7 1.4e-5 3.8e-4 4.1e-4 4.1e-4 4.2e-4 4.4e-4   1095    1.0\n",
      "u_predict[186,5] 4.1e-4  4.3e-7 1.4e-5 3.8e-4 4.0e-4 4.1e-4 4.2e-4 4.4e-4   1095    1.0\n",
      "u_predict[187,5] 4.1e-4  4.3e-7 1.4e-5 3.8e-4 4.0e-4 4.1e-4 4.2e-4 4.4e-4   1095    1.0\n",
      "u_predict[188,5] 4.1e-4  4.3e-7 1.4e-5 3.8e-4 4.0e-4 4.1e-4 4.2e-4 4.3e-4   1095    1.0\n",
      "u_predict[189,5] 4.1e-4  4.3e-7 1.4e-5 3.8e-4 4.0e-4 4.1e-4 4.2e-4 4.3e-4   1095    1.0\n",
      "u_predict[190,5] 4.1e-4  4.3e-7 1.4e-5 3.8e-4 4.0e-4 4.1e-4 4.2e-4 4.3e-4   1095    1.0\n",
      "u_predict[191,5] 4.1e-4  4.3e-7 1.4e-5 3.8e-4 4.0e-4 4.1e-4 4.2e-4 4.3e-4   1095    1.0\n",
      "u_predict[192,5] 4.1e-4  4.3e-7 1.4e-5 3.8e-4 4.0e-4 4.1e-4 4.2e-4 4.3e-4   1095    1.0\n",
      "u_predict[193,5] 4.1e-4  4.3e-7 1.4e-5 3.8e-4 4.0e-4 4.1e-4 4.2e-4 4.3e-4   1095    1.0\n",
      "u_predict[194,5] 4.1e-4  4.3e-7 1.4e-5 3.7e-4 4.0e-4 4.1e-4 4.2e-4 4.3e-4   1095    1.0\n",
      "u_predict[195,5] 4.1e-4  4.3e-7 1.4e-5 3.7e-4 4.0e-4 4.1e-4 4.2e-4 4.3e-4   1095    1.0\n",
      "u_predict[196,5] 4.1e-4  4.3e-7 1.4e-5 3.7e-4 4.0e-4 4.1e-4 4.2e-4 4.3e-4   1095    1.0\n",
      "u_predict[197,5] 4.1e-4  4.3e-7 1.4e-5 3.7e-4 4.0e-4 4.1e-4 4.1e-4 4.3e-4   1095    1.0\n",
      "u_predict[198,5] 4.1e-4  4.3e-7 1.4e-5 3.7e-4 4.0e-4 4.1e-4 4.1e-4 4.3e-4   1095    1.0\n",
      "u_predict[199,5] 4.0e-4  4.3e-7 1.4e-5 3.7e-4 4.0e-4 4.1e-4 4.1e-4 4.3e-4   1095    1.0\n",
      "u_predict[200,5] 4.0e-4  4.3e-7 1.4e-5 3.7e-4 4.0e-4 4.1e-4 4.1e-4 4.3e-4   1095    1.0\n",
      "u_predict[201,5] 4.0e-4  4.3e-7 1.4e-5 3.7e-4 4.0e-4 4.1e-4 4.1e-4 4.3e-4   1095    1.0\n",
      "u_predict[202,5] 4.0e-4  4.3e-7 1.4e-5 3.7e-4 4.0e-4 4.0e-4 4.1e-4 4.3e-4   1095    1.0\n",
      "u_predict[203,5] 4.0e-4  4.3e-7 1.4e-5 3.7e-4 4.0e-4 4.0e-4 4.1e-4 4.3e-4   1095    1.0\n",
      "u_predict[204,5] 4.0e-4  4.3e-7 1.4e-5 3.7e-4 4.0e-4 4.0e-4 4.1e-4 4.3e-4   1095    1.0\n",
      "u_predict[205,5] 4.0e-4  4.3e-7 1.4e-5 3.7e-4 3.9e-4 4.0e-4 4.1e-4 4.3e-4   1095    1.0\n",
      "u_predict[206,5] 4.0e-4  4.3e-7 1.4e-5 3.7e-4 3.9e-4 4.0e-4 4.1e-4 4.3e-4   1095    1.0\n",
      "u_predict[207,5] 4.0e-4  4.3e-7 1.4e-5 3.7e-4 3.9e-4 4.0e-4 4.1e-4 4.2e-4   1095    1.0\n",
      "u_predict[208,5] 4.0e-4  4.3e-7 1.4e-5 3.7e-4 3.9e-4 4.0e-4 4.1e-4 4.2e-4   1095    1.0\n",
      "u_predict[209,5] 4.0e-4  4.3e-7 1.4e-5 3.7e-4 3.9e-4 4.0e-4 4.1e-4 4.2e-4   1094    1.0\n",
      "u_predict[210,5] 4.0e-4  4.3e-7 1.4e-5 3.7e-4 3.9e-4 4.0e-4 4.1e-4 4.2e-4   1094    1.0\n",
      "u_predict[211,5] 4.0e-4  4.3e-7 1.4e-5 3.7e-4 3.9e-4 4.0e-4 4.1e-4 4.2e-4   1094    1.0\n",
      "u_predict[212,5] 4.0e-4  4.3e-7 1.4e-5 3.7e-4 3.9e-4 4.0e-4 4.1e-4 4.2e-4   1094    1.0\n",
      "u_predict[213,5] 4.0e-4  4.3e-7 1.4e-5 3.7e-4 3.9e-4 4.0e-4 4.1e-4 4.2e-4   1094    1.0\n",
      "u_predict[214,5] 4.0e-4  4.3e-7 1.4e-5 3.7e-4 3.9e-4 4.0e-4 4.1e-4 4.2e-4   1094    1.0\n",
      "u_predict[215,5] 4.0e-4  4.3e-7 1.4e-5 3.7e-4 3.9e-4 4.0e-4 4.1e-4 4.2e-4   1094    1.0\n",
      "u_predict[216,5] 4.0e-4  4.3e-7 1.4e-5 3.7e-4 3.9e-4 4.0e-4 4.1e-4 4.2e-4   1094    1.0\n",
      "u_predict[217,5] 4.0e-4  4.3e-7 1.4e-5 3.7e-4 3.9e-4 4.0e-4 4.1e-4 4.2e-4   1094    1.0\n",
      "u_predict[218,5] 4.0e-4  4.3e-7 1.4e-5 3.7e-4 3.9e-4 4.0e-4 4.1e-4 4.2e-4   1094    1.0\n",
      "u_predict[219,5] 4.0e-4  4.3e-7 1.4e-5 3.7e-4 3.9e-4 4.0e-4 4.1e-4 4.2e-4   1094    1.0\n",
      "u_predict[220,5] 4.0e-4  4.3e-7 1.4e-5 3.7e-4 3.9e-4 4.0e-4 4.1e-4 4.2e-4   1094    1.0\n",
      "u_predict[221,5] 4.0e-4  4.3e-7 1.4e-5 3.7e-4 3.9e-4 4.0e-4 4.1e-4 4.2e-4   1094    1.0\n",
      "u_predict[222,5] 4.0e-4  4.3e-7 1.4e-5 3.7e-4 3.9e-4 4.0e-4 4.1e-4 4.2e-4   1094    1.0\n",
      "u_predict[223,5] 4.0e-4  4.3e-7 1.4e-5 3.6e-4 3.9e-4 4.0e-4 4.1e-4 4.2e-4   1094    1.0\n",
      "u_predict[224,5] 4.0e-4  4.3e-7 1.4e-5 3.6e-4 3.9e-4 4.0e-4 4.1e-4 4.2e-4   1094    1.0\n",
      "u_predict[225,5] 4.0e-4  4.3e-7 1.4e-5 3.6e-4 3.9e-4 4.0e-4 4.1e-4 4.2e-4   1094    1.0\n",
      "u_predict[226,5] 4.0e-4  4.3e-7 1.4e-5 3.6e-4 3.9e-4 4.0e-4 4.1e-4 4.2e-4   1094    1.0\n",
      "u_predict[227,5] 4.0e-4  4.3e-7 1.4e-5 3.6e-4 3.9e-4 4.0e-4 4.1e-4 4.2e-4   1094    1.0\n",
      "u_predict[228,5] 4.0e-4  4.3e-7 1.4e-5 3.6e-4 3.9e-4 4.0e-4 4.1e-4 4.2e-4   1094    1.0\n",
      "u_predict[229,5] 4.0e-4  4.3e-7 1.4e-5 3.6e-4 3.9e-4 4.0e-4 4.0e-4 4.2e-4   1094    1.0\n",
      "u_predict[230,5] 4.0e-4  4.3e-7 1.4e-5 3.6e-4 3.9e-4 4.0e-4 4.0e-4 4.2e-4   1094    1.0\n",
      "u_predict[231,5] 4.0e-4  4.3e-7 1.4e-5 3.6e-4 3.9e-4 4.0e-4 4.0e-4 4.2e-4   1094    1.0\n",
      "u_predict[232,5] 4.0e-4  4.3e-7 1.4e-5 3.6e-4 3.9e-4 4.0e-4 4.0e-4 4.2e-4   1094    1.0\n",
      "u_predict[233,5] 4.0e-4  4.3e-7 1.4e-5 3.6e-4 3.9e-4 4.0e-4 4.0e-4 4.2e-4   1094    1.0\n",
      "u_predict[234,5] 4.0e-4  4.3e-7 1.4e-5 3.6e-4 3.9e-4 4.0e-4 4.0e-4 4.2e-4   1094    1.0\n",
      "u_predict[235,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.9e-4 4.0e-4 4.0e-4 4.2e-4   1094    1.0\n",
      "u_predict[236,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.9e-4 4.0e-4 4.0e-4 4.2e-4   1094    1.0\n",
      "u_predict[237,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.9e-4 4.0e-4 4.0e-4 4.2e-4   1094    1.0\n",
      "u_predict[238,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.9e-4 4.0e-4 4.0e-4 4.2e-4   1094    1.0\n",
      "u_predict[239,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.9e-4 4.0e-4 4.0e-4 4.2e-4   1094    1.0\n",
      "u_predict[240,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.9e-4 4.0e-4 4.0e-4 4.2e-4   1094    1.0\n",
      "u_predict[241,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.9e-4 4.0e-4 4.0e-4 4.2e-4   1094    1.0\n",
      "u_predict[242,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.9e-4 4.0e-4 4.0e-4 4.2e-4   1094    1.0\n",
      "u_predict[243,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.9e-4 4.0e-4 4.0e-4 4.2e-4   1094    1.0\n",
      "u_predict[244,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.9e-4 4.0e-4 4.0e-4 4.2e-4   1094    1.0\n",
      "u_predict[245,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.9e-4 4.0e-4 4.0e-4 4.2e-4   1094    1.0\n",
      "u_predict[246,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.9e-4 4.0e-4 4.0e-4 4.2e-4   1094    1.0\n",
      "u_predict[247,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.9e-4 3.9e-4 4.0e-4 4.2e-4   1094    1.0\n",
      "u_predict[248,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.9e-4 3.9e-4 4.0e-4 4.2e-4   1094    1.0\n",
      "u_predict[249,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.9e-4 3.9e-4 4.0e-4 4.2e-4   1094    1.0\n",
      "u_predict[250,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.9e-4 3.9e-4 4.0e-4 4.2e-4   1094    1.0\n",
      "u_predict[251,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.9e-4 3.9e-4 4.0e-4 4.2e-4   1094    1.0\n",
      "u_predict[252,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.9e-4 3.9e-4 4.0e-4 4.2e-4   1094    1.0\n",
      "u_predict[253,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.9e-4 3.9e-4 4.0e-4 4.2e-4   1094    1.0\n",
      "u_predict[254,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.9e-4 3.9e-4 4.0e-4 4.2e-4   1094    1.0\n",
      "u_predict[255,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.9e-4 3.9e-4 4.0e-4 4.2e-4   1094    1.0\n",
      "u_predict[256,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.9e-4 3.9e-4 4.0e-4 4.2e-4   1094    1.0\n",
      "u_predict[257,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.9e-4 3.9e-4 4.0e-4 4.2e-4   1094    1.0\n",
      "u_predict[258,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.9e-4 3.9e-4 4.0e-4 4.2e-4   1094    1.0\n",
      "u_predict[259,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.9e-4 3.9e-4 4.0e-4 4.2e-4   1094    1.0\n",
      "u_predict[260,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.9e-4 3.9e-4 4.0e-4 4.2e-4   1094    1.0\n",
      "u_predict[261,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.9e-4 3.9e-4 4.0e-4 4.2e-4   1094    1.0\n",
      "u_predict[262,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.9e-4 3.9e-4 4.0e-4 4.2e-4   1094    1.0\n",
      "u_predict[263,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.9e-4 3.9e-4 4.0e-4 4.2e-4   1094    1.0\n",
      "u_predict[264,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.2e-4   1094    1.0\n",
      "u_predict[265,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.2e-4   1094    1.0\n",
      "u_predict[266,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.2e-4   1094    1.0\n",
      "u_predict[267,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.2e-4   1094    1.0\n",
      "u_predict[268,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[269,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[270,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[271,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[272,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[273,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[274,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[275,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[276,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[277,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[278,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[279,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[280,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[281,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[282,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[283,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[284,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[285,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[286,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[287,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[288,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[289,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[290,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[291,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[292,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[293,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[294,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[295,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[296,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[297,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[298,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[299,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[300,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[301,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[302,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[303,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[304,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[305,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[306,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[307,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[308,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[309,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[310,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[311,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[312,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[313,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[314,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[315,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[316,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[317,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[318,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[319,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[320,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[321,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[322,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[323,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[324,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[325,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[326,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[327,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[328,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[329,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[330,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[331,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[332,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[333,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[334,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[335,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[336,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[337,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[338,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[339,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[340,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[341,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[342,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[343,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[344,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[345,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[346,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[347,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[348,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[349,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[350,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[351,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[352,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[353,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[354,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[355,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[356,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[357,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[358,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[359,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[360,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[361,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[362,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[363,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[364,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[365,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[366,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[367,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[368,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[369,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[370,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[371,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[372,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[373,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[374,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[375,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[376,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[377,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[378,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[379,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[380,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[381,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[382,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[383,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[384,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[385,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[386,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[387,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[388,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[389,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[390,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[391,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[392,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[393,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[394,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[395,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[396,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[397,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[398,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[399,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[400,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[401,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[402,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[403,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[404,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[405,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[406,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[407,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[408,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[409,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[410,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[411,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[412,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "u_predict[413,5] 3.9e-4  4.3e-7 1.4e-5 3.6e-4 3.8e-4 3.9e-4 4.0e-4 4.1e-4   1094    1.0\n",
      "ll_sigma           0.03  1.4e-6 5.5e-5   0.03   0.03   0.03   0.03   0.03   1424    1.0\n",
      "ll_beta            0.25  1.7e-5 5.9e-4   0.24   0.25   0.25   0.25   0.25   1234    1.0\n",
      "lp__              -3626    0.05    1.7  -3631  -3627  -3626  -3625  -3624   1414    1.0\n",
      "\n",
      "Samples were drawn using NUTS at Wed Apr  8 23:09:48 2020.\n",
      "For each parameter, n_eff is a crude measure of effective sample size,\n",
      "and Rhat is the potential scale reduction factor on split chains (at \n",
      "convergence, Rhat=1).\n"
     ]
    }
   ],
   "source": [
    "print(fit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x1368faa10>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x135e95250>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x135ee4d90>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x135e3c850>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x135e0ed90>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x135dc2410>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x135dbac50>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1490.4x993.6 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#https://arviz-devs.github.io/arviz/generated/arviz.plot_density\n",
    "az.plot_density(fit,group='posterior',var_names=[\"theta\",\"R_0\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "48\n",
      "(77, 4)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x13a8f77d0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x13a893990>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x13a7c9a50>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x135e7fd90>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x13bd94d90>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x13a8965d0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x1394b4d10>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Rth46wc2zNhLg68PMGwfRO65h5380RHFFLTe8vZ49R0p4+dr+nNeznbEsItIk1GgX8WS7Dxcza3Umn24/TK3Dybk92nLn6ERSOrQ0lun5xQd44buD/OPqfkZmEoh4EdMDdtVtES+XdryM+RsO8eHmHE5U1JLcNoxpY7pwYZ+mX0n3U2odTq57az07cov4eNpwurfXdnbiMkzX7fpUx8XtrUsv4JZZG4kOC2TOrWfRoVWI6UgUV9Zy49sb2JVbzFs3DmRs1zamI4lI41GjXcTT1DmcLN6TxztrMtmQUUiwvy+X9Y/l5uGd6dImzHQ8HE6bK15fQ1ZBBd89MJqWoY1z6KqI/BfTA3bVbREvlJlfztL9x/hq11E2ZBTi52Nxbs+2XDOoAyO6ROPTRHuwn47jpdVMfHElUSEBfHr3cIL8dTiquATzvxz/SXVc3NqmzEJueHsDMZHBvDflLNqEB5mO9IOSqlomv7mO9OPlvHfbWUYnwolIo1KjXcRT5JVUMW/DIeZvyOZoSRVxLYO5cWgnrhoYT0SIv+l4/2HvkRIufGkVVw6I45nL+5iOI+KpTA/YVbdFvEBVrYP1GYUs3XeMZfuPkVlQAUBi61AuHxDHlQPiad0i0HDK/7Z0/zFufmcjt43szG8v0H7t4hJM1+36VMfFbW3LLuL6Getp0yKQ+XcMoU0L12myf+9YaRVXvLaW0qpaPpo6jITW5ifFiUiDqdEu4s5s22ZDRiGz1mSyaE8eDqfNqOTWXH9WB8Z1b4uvC8wa+ylPfbmXN1ek8687hzKoU5TpOCKeyPQLgOq2iIfKLqxg2f5jLN1/nDVp+VTVOgny92FoQivGdmvDmOQ2LrE8/+f87pOdzF1/iPemDGFoYivTcURM1+36VMfFLWUVlDPp1TWEBvryrzuG0S7C9Zrs38vML2fSq6tpGRrAx9OGExHsWhPkROS0qdEu4o4cTpsvdx7hrZXp7MgpJjLEn6sGxnPt4A50ig41He8Xqaip45zpK2gZ6s9nd41wiaXkIh7G9C+V6raIB9l/tJSPt+by7d48Uo+VAdAhKoSzu7VhTNfWDElo5XZbsFTU1DHhhZX4WhZf3Tey2Q9mFanHdN2uT3Vc3E5heQ2Xv7aGExU1LHCTWeLr0gu4fsZ6hneJ5u2bBrn0ZDkR+VlqtIu4k5o6Jx9vzeH15elk5JeTEB3KrSM7c1lKHMEB7jc4/GRrLve9v43nr+7LpJQ403FEPI3pq3TVbRE3d7y0ms+2H2bBlhx2Hy7Bz8diaGIrxnY92VzvHB2KZZl+qWmYFQeOc8PbG3jgnGTuGZdkOo54N1f7ZVIdF7ficNpcP2M9mw+d4L0pZzHQjVZNv7f+EI9/vJOpYxJ55PxupuOIyJlTo13EHVTU1DFvQzZvrUjnaEkVvWMjmDYmkXN7tnPrd7ydTptLXllNQVk1Sx4c43Yz4URcnOkXB9VtETe1M6eYmavSWbjjCHVOmz5xEVyWEstFfWNoFeZ6+6031F1zt7B4bx6L7x9Fx1busTJQPJLpul2f6ri4lecW7eelJan8/Yo+XDkw3nSc0/bYgh3M25DNzBsHMq57W9NxROTMqNEu4sqKK2p5d20m76zO4ERFLWd1juKusV0YmRTt9jPIvrc2rYDJb63jkfO7MXVMouk4Ip7E9IuE6raIG7Ftm8V78pixKoMNGYWEBvhy5cB4rj2rA8ltW5iO16SOFlcxfvpyBneO4u2bBpmOI97LdN2uT3Vc3May/ce46Z2NXDUwjr9d0dd0nDNSVevgslfXkFtUycJfjyA+yvXPOhGR/6JGu4grOlZSxcxVGcxZl0V5jYNx3dowbWwiAzq6z/K303HrrI1syjrBykfGEh6kA2BEGonpAbvqtoibyMgv57EFO1iXXkhsZDA3DevE1YPjvaomv748jWe+2sfsWwYzKrm16TjinUzX7fpUx8UtHC6q5IIXV9I2PIiPpw13yy1Vv5dVUM6FL64iuV0L3r99CH6+PqYjicjpUaNdxJUcKa7klaWpfLAphzqHk4v6xjB1TCLd2oWbjtakduUWc+FLq7h/fDL3jtf+qCKNxPSAXXVbxMXVOpy8uSKdF747SKCfD49P7M6VA+K8cmBfXefgnOkrCPL34ct7Rnrl/wMxznTdrk91XFxercPJNW+uY9+REj779QgS3eDw05/z6bZc7p2/TWNjEffUqLXcrzEfTMSbFJbX8OrSVGavy8K2ba4YEM+doxO8Zp/QXrERnNujLTNWpXPTsE5EhHjPDDoRERETduUW8+C/trPvaCkTe7fjiYt60iY8yHQsYwL9fHl8YnfunLOZeRsO8auhnUxHEhGRn/HsN/vZnHWCFyeneESTHeCSfrEs2XeMF5ccZGRyNP07tDQdSUQM0Yx2kdNUXl3HWyvTmbEyg4qaOi7rH8d945OIa+l9+7HtOVzCxBdX8uuzu/Cbc7uajiPiCUzPjFPdFnFBtm0za00mT3+5j8gQf568tBfn9WxnOpZLsG2ba95cR+qxMpY/PJawQM0jkmZlum7XpzouLm1DRiFXv7mWyYM78NSk3qbjNKriylomvrASXx+LL+8dqXok4j4atZZrfaXIaVi8J49zpi/nH98eZESXaL65bxTPXtnXK5vsAD1iwpnQqx2zVmdSUlVrOo6IiIjHKauuY+qcLfzx8z2MTDp57aEm+/9nWRaPTuhGQXkNb6/KMB1HRER+QkVNHQ99uJ24lsH8dmJ303EaXUSwP89f3Y+cExX88bPdpuOIiCFqtIv8AoeLKrl99iZum72JsCA//nXnUF7/1QCS2rYwHc24aWO6UFpdx3vrD5mOIiIi4lEy88uZ9MpqFu/N47cTuzPjxoG0DA0wHcvlpHRoyfk92/HminQKy2tMxxERkR/x92/2k1VQwd8u70uoh872Htw5imljuvCvzTl8ufOI6TgiYoAa7SL/g9NpM2t1BuOnL2fFweM8cn43vrhnJIM6RZmO5jJ6x0Uwoks0M1dlUFXrMB1HRETEIyzbf4yLX17F8bJqZt8ymNtGJWBZrrZLhet48LxkKmrqeGVpqukoIiJSz86cYmatyeSGoR0ZmtjKdJwmde/4JPrGRfDYgp0cKa40HUdEmpka7SI/IbeokutnrueJz/cwqFMUi+8fzdQxifj76temvqljEjleWs3HW3NNRxEREXFrtm3z2rI0bp61kZjIYD6/ewTDu0SbjuXyurRpweX945izLotjpVWm44iIyClOp83vP91Fq9BAHjzP88/18vf14R/XpFBT5+SB97fjdOroBBFvoo6hSD22bfPh5hzOf34F27OLePqy3sy6eRDxUd65D/svMSyxFX3iInhjeRoOXUiIiIickYqaOu6et5W/fr2Pib3bs2DaMF1/nIa7xnah1uFkxkrt1S4i4io+3JLDtuwiHpvQjfAgf9NxmkXn6FCeuLgHa9MLeGtluuk4ItKM1GgX+Tdl1XVMm7uFB/+1ne4x4Xx93ygmD+6gpdo/w7Ispo5OJLOggq93HTUdR0RExO1kF1Zw2atr+HLnER45vxsvT04hJMAz97BtKp2iQ7mkXyxz1mVpr3YRERdQXFnLX7/ax8COLbmsf6zpOM3qqoHxnNezLc8u2s+u3GLTcUSkmajRLnJKxqkDxxbtyeOxCd2Yf9sQzSI7Def2bEdCdCivL0/DtjWrXURE5JdanZrPRS+v4nBRJe/cNIipYxL1Jv8ZmjYmkcpaBzNXaQahiIhpzy8+wImKGv54SU+vq2uWZfHMZX2ICg3g3vlbqazReWYi3kCNdhFg6akDx/LLqvnnrYO5Y3QiPj7edSHQUL4+FrePSmBnbjGrUwtMxxEREXF5tm0zc1UGN7y9gdZhgXx69wjGdG1jOpZbS2rbggm92vHumiyKK2pNxxER8Vp7j5Qwe20m153VkZ4xEabjGNEyNIBnr+xL2vFynvpyr+k4ItIM1GgXr/b9gWO3zNpIfMsQPrt7BMMSdeDYmZrUP5Y2LQJ5bXmq6SgiIiIurbLGwW8+2M6TC/cwrlsbPr5rOJ2jQ03H8gh3j02irLqOWWsyTUcREfFKtm3zh093ExkSwG/OTTYdx6iRSa2ZMqIz/1yXxXd780zHEZEmpka7eC2H0+b/Pt3NX7/ex4V9Yvhoqg4ca6hAP1+mjOzM6tQCduQUmY4jIiLiknYfLubCl1ayYGsu949P5vXrBxAWqP3YG0uPmHDGd2/D26szKKuuMx1HRMTrfL3rKBsyC3novK5EhgSYjmPcQ+d3pVu7Fjz84Q6Ol1abjiMiTUiNdvFKNXVO7pm/lX+uy+KO0Qm8eE0/ggN8TcfyCJMHdyA8yI/XlqWZjiIiIuJSnM6TW8VMemUNpVV1zLn1LO4dn6Tt6prA3WcnUVxZyz/XZpmOIiLiVeocTv6+aD9JbcK4amC86TguIdDPlxcnp1BWXcdDH27XmWYiHkyNdvE6VbUO7pyzmS92HOGxCd14bEJ3rzuYpSm1CPLnhqGd+Hr3UdKOl5mOIyIi4hKOlVRx86yNPLlwD6OSW/P1faMYkaTt6ppKv/hIRiZFM2Nlug6gExFpRh9tySH9eDkPndcVX72R/IPkti14fGJ3lu0/zhsrdGC3iKdSo128SkVNHbe+u5Gl+4/xl0m9uGN0oulIHumm4Z0I8PXhzeW6gBAREe/mdNrMWZfFuOnLWZdewJ8v7cVbNwwgKlRL6Zva3WO7UFBew4ebs01HERHxClW1Dp5ffJCUDpGc06Ot6Tgu54ahHbmgd3v+9vU+1qYVmI4jIk1AjXbxGiVVtdwwcwNr0wp47sq+XHdWR9ORPFZ0WCBXD4pnwdYcjhZXmY4jIiLS7JxOm693HeGCl1bxu0920Ts2gq/vG8X1QzpqJV0zGdw5ir7xkcxYlYHDqWX6IiJNbfbaTI6WVPHI+d1U636EZVn89Yo+dI4O5dfztpBbVGk6kog0MjXaxSsUVdRw/Yz1bMsu4qXJ/bmsf5zpSB7vtpEJOG2YuUqz2kVExHs4nTZf7jzCxBdXcuecLVTXOnjhmn7MnXIWnaNDTcfzKpZlcceoBLIKKvhm91HTcUREPFpxZS2vLE1jdHJrhiS0Mh3HZYUF+vHGrwZQXefkhpnrOVFeYzqSiDQiNdrF45VX13HTOxvZd6SU168fwAV92puO5BXio0K4qE973lt/iKIKXTyIiIjnsm2brYdO8PSXexnz7DKmzd1CjcPJP67ux+IHRnNJv1jN7DPkvJ7t6NgqhDdWpOvwORGRJvTWinSKK2t56LyupqO4vC5tWjDjhoFkn6jklnc3UlFTZzqSiDQSNdrFo1XVOrj9n5vYmVvMS9emMF77xDWrO8ckUl7j4J9rs0xHERERaTROp012YQWfbz/M/326i2HPLGHSq2uYuSqDTtGhvDg5hcX3j+bSlFgdBGeYr4/FlJEJbM8uYkNGoek4IiIe6VhpFTNXZXBR3xh6xUaYjuMWzkpoxYvXpLA9u4hbZ21Ss13EQ1hNPLND00bEmDqHk2lzt7BoTx7PXdmXywdouxgTbpm1kW3ZRax+5GyCA3xNxxFxdaY7cqrb4tVs26akqo7C8hoKy6spKKuhsLyGgvIajpdWc6iwgqyCcrJPVFJT5wQg2N+XEUnRTOjVjnHd2hIR4m/4u5D6qmodDHtmCSnxkcy8aZDpOOJZTNft+lTHxYg/fr6b2Wuz+PaB0dom7TR9sjWXBz7YxlmdWzHzpoGEBPiZjiTibRq1lus3WDyS02nz8Ec7WLQnjycu6qEmu0FTxyRy5etr+WBTNjcO62Q6joiIeLniylq2HjrBvqOlHMgrJa+k6oeG+omKGmodP96nCgv0Iz4qhKQ2LRjfvS0dWoXQJzaSbu1b4O+rRaKuLMjflxuGduQf3x7kYF4pSW1bmI4kIuIxjpdWM2/DIS7tF6sm+xm4NCUWgAc+2Mblr63lzV8NID4qxHAqETlTmtEuHunJhXuYuSqD+8cnc+/4JNNxvN4Vr63hSHEVyx4ao2aEyP9memac6rZ4nLLqOpbtP8aGjEI2ZBSyP6+U7y9/24UHERMZRFRoIK1CA4gKCzj58dSfVqGBP9wW5K9VWe6ssLyGYc98x0V9Yvj7lX1NxxHPYbpu1zd8ONUAACAASURBVKc6Ls3u6a/28uaKdL57YDQJrcNMx3FbS/cf4955W/HxsZh+VV/O7qZtb0WaSaPWcjXaxeO8szqDP36+h5uGdeIPF/XQ4WMu4Lu9edz67iaev7ovk1K0ukDkfzD9gqW6LR6hps7Jsv3H+HT7Yb7dk0d1nZOQAF8GdGzJwI5RDOrckp4xEUQEa5sXb/L7T3Yxf+MhVj1yNm3Dg0zHEc9gum7XpzouzepEeQ3D/7qEcd3b8tLkFNNx3F5WQTl3ztnC3iMlTB4cz28v6EFYoDaiEGliarSL/JRFu49yx5zNnNO9La9dP0AHkLkIp9NmwgsrsbH5+t5R+OjfReSnmP7lUN0Wt3a4qJK3V2Xwr805FFfWEhUawIV92nNR3xhS4iPx06oqr5ZVUM7YZ5dx+6hEHp3QzXQc8Qym63Z9quPSrKYv2s+LS1L55r5RdG2nbbkaQ3Wdg+mLD/DminRahwXy4LlduXxAnHobIk1HjXaRH7M9u4ir31xL17YtmH/7UB286WI+3prD/e9v581fDeDcnu1MxxFxVaavoFW3xS3llVTx/OIDfLg5Bxs4v1c7rugfx4ikaG1ZJv9h2tzNrDyYz9rHxmmWoDQG03W7PtVxaTYVNXUMe2YJAztGMePGgabjeJyth07wp4V72HqoiE6tQrhyYDyTUmKJiQw2HU3E06jRLlJfdmEFk15dTXCALwumDqd1i0DTkaSeWoeT8dOXExrgx8Jfj9CsdpEfZ/oXQ3Vb3Eqdw8nry9N4ZWkadU4n153VkSkjOxPXUoeIyY/bll3Epa+s5ncXdGfKyATTccT9ma7b9amOS7OZtTqDJz7fw4d3DmVgpyjTcTySbdt8teso767JZH1GIQC9YyMY260NKR0i6RsXSVRogOGUIm5PjXaRf1dcUctlr63meGk1C6YNp0sbHcDiqhZsyeGBD7bz2nX9mdC7vek4Iq7I9IBddVvcxqGCCu7/YBubs04woVc7HpvQnQ6t1GCXn3f1G2vJLqxg+cNjteJBGsp03a5PdVyaRZ3DyZhnl9E2PIiPpg4zHccrZBWU88XOIyzek8e27KIfDnbv2CqEPnGRdG0bRlLbFiS1CaNjq1BtNSPyy6nRLvK96joHN8zcwNZDRcy+dTBDElqZjiT/g8Npc+7zy/GxLL6+b5SKv8h/M/1LobotLs+2bRZsyeUPn+3GsuDPl/bikn6xpmOJG1myL49bZumQdmkUput2farj0iw+236Ye+Zt5Y1fDeA8bQva7Mqq69iZU8z2nCK2ZxexI6eY3KLKH+4P8PMhITqU5LYtSG4bRpc2Jz92iArReTUi/02NdhE4OdB+4IPtfLw1lxeu6adBtptYuOMwd7+3lX9c3Y9LU/RvJlKP6QG76ra4tPLqOh5dsJPPtx9mcOcopl/VV9vEyGlzOm3O+8cK/Hx9+PKeEViW6ZdecWOu9sOjOi5NzrZtLnxpFZU1Dr59YLS2BHURZdV1pB0r40BeKamnPh48VkbOif9uwCe1bUG3di3oFRtB79gIbT8j3q5RX8R0ApC4rTdXpPPx1lx+c06ymuxuZGKv9vSMSePv3+zn/F7tCPLXobUiIvLzsgsruG32Jg7klfLQeV25c3SiVkbJGfHxsbhtZAIPf7SD1akFjEiKNh1JRMRtrEkrYPfhEp6+rLea7C4kLNCPvvGR9I2P/I/by6vrSDtexoG8Mg4eK+VgXhlbD53g8+2Hf/ic2MhgesWG0zs24ofme6swnXsnciY0o13c0vIDx7n5nQ1M6N2elyenaCaSm1mTls+1b63nofO6ctfYLqbjiLgS0y9mqtvikrYcOsGtszbicNq8fG1/RiW3Nh1J3Fx1nYPhzyylR0w4s28ZbDqOuC/Tdbs+1XFpcr+auZ69R0pZ9chYTZpyYyVVtezOLWFXbjE7T/3JyC//4f72EUF0aRNGYusw+sRFMLhzlFYRiqfS1jHi3TLzy7n45VXERAazYNowQgK0MMMd3TZ7E2tS81n20Fhat9C75SKnmB6wq26Ly1myL49pc7fQNjyIWTcPpnN0qOlI4iFeWZrK37/Zz1f3jqR7+3DTccQ9ma7b9amOS5Pac7iEiS+u1IQpD/Xvzfddh4tJO15G+vFyKmocAMREBDGwUxTDElsxKrk1MZHBhhOLNAo12sV7lVXXMemV1Rwvq+bzu0cQH6V3VN1VRn4550xfzqSUWP5+ZV/TcURchekBu+q2uJSvdh7h7nlb6dE+nLdvGqQ3ZqVRFVXUMPTpJUzs3Z7nrtK1iJwR03W7PtVxaVL3zd/Koj15rH10HBEh/qbjSDNwOG32Hy1lY2YhGzIL2ZhRyLHSagB6tA/n8gFxXNovRlvNiDtTo128k9NpM3XuZr7de4zZtwxmeBftp+nunvlqH68vT+O9KWcxTP+eImB+wK66LS7j2z153DlnM33jI3n3lsGEBWoFmzS+Jz7bzdz1Wax8+GzaRQSZjiPux3Tdrk91XJpMzokKRv99GTcN68TvL+xhOo4YYts2B4+VsXz/cT7fcZgdOcUE+PlwxYA4bh+ZQCetPBT306i13KcxH0ykKb2xIp1vdufx2IRuarJ7iPvGJ9GxVQiPfbyTqlqH6TgiIuIi1qTlM23uFnrGhPPOzYPUZJcmc8vwzjicNrPWZJqOIiLi0t5elQnALSM6mw0iRlmWRXLbFtw2KoHP7h7BN/eN4vL+cXy4KYdx05fz5MI9lFbVmo4pYowa7eIW1qcX8Oyi/VzQpz23qrB7jCB/X56e1JusggqmLz5gOo6IiLiAfUdLuGP2ZjpFh/DuLYMJD9LSdGk6HVqFMKFXe+auz6Ksus50HBERl1RcUcv8jYe4uG8MsdqXW/5N13YtePqy3qx6ZCxXDYzn7dUZnP3ccj7emkMT76Ah4pLUaBeXl19Wza/nbaVDVAjPXNYby3K1FZrSEMO6RDN5cAfeWpnOyoPHTccRERGDjhRXcvM7GwkJ9GXWzYOJDAkwHUm8wJSRnSmtqmP+hkOmo4iIuKQ567OoqHFw28gE01HERbUJD+Lpy3rzybThxEQGc//727nqjbVk5pebjibSrNRoF5fmcNrcO38rxZW1vHJtf1poVptH+r8Le9CldRj3v7+d46cOVhEREe9SWlXLze9spLSqjnduGkyMZsxJM0np0JLBnaOYsTKDmjqn6TgiIi6lqtbBO6szGZXcmh4x4abjiIvrGx/Jx1OH8bfL+3Agr4yLXlrF17uOmI4l0mzUaBeX9uJ3B1mdWsCfLumpou7BggN8efna/pRW1XLPvK3UOjTIFRHxJjV1TqbO2ULqsTJeu76/ar40u7vGduFoSRULtuSYjiIi4lI+3ppLflk1d4zSbHb5ZXx8LK4aFM8X94wgoXUod87ZwnOL9msrGfEKarSLy1qdms+LSw5yWf9YrhoYbzqONLHv93Zbm17Aox/tVBEWEfEif/x8N6tS83nm8j6MTGptOo54oVFJ0fSOjeC15WnU6Q1/EREAnE6bt1ak0ys2nGGJrUzHETcT1zKED+4cylUD43hpSSq/+WC7Vo6Jx1OjXVxScUUtv/lgO52jQ/nzpb20L7uXuKx/HPePT+ajLTk6HFVExEt8ui2XuesPccfoBK4YEGc6jngpy7K4a2wiWQUVfLFTS9xFRAAW780jPb+c20clakwuZyTQz5e/Xt6HB85JZsHWXG58ewPFlbWmY4k0GTXaxSU98flujpdV8/xV/QgJ8DMdR5rRPeO6cM2geF5aksrTX+7VzHYREQ+WdryMxxfsZGDHljx0blfTccTLndujHUltwnh1aRpOp64/RETeXJFOXMtgJvZqZzqKuDHLsrhnXBLPXdmXjZmFXPn6GnKLKk3HEmkSarSLy/ly5xE+3prL3WO70Dc+0nQcaWaWZfGXSb25YWhH3liRzm/+tZ2KmrpGfx6n0+ZwUSW7cotZk5bPxsxCdh8uJr+sWs19EZFmUFXr4K65Wwj09+Wla1Pw89VlqZjl42MxbWwi+/NK+XZvnuk4IiJGbcosZHPWCaaM6KwaLY3i8gFxzL5lMEeKqrjq9bVkF1aYjiTS6KwmbiipWyWn5VhpFec9v4K4liEsmDYMfxV0r2XbNi9+l8rz3x4gNjKYP0/qxdiubc7osZxOm7TjZWw5dIItWUVszykiI7+c6p/YHy4s0I9+8ZFM6N2OCb3aExUa0JBvReR0mF6Tq7otzeaxBTuYtyGbWTcPYswZvr6LNLY6h5Oxzy0jKiSAT+4arq0S5Oe42g+I6rg0minvbmJTViFrHj1bq8ylUe3MKea6GetoEeTP/NuHEB8VYjqSeLdGreVqtIvLsG2bKe9uYmVqPl/eM4IubVqYjiQuYENGIY8t2EHa8XIGdGzJ7aMSGJ3cmiB/35/8msLyGnbmFrP10Am2HCpi26ETlFSdnBUfGeJP37hIktuG0Sk6lFahgYQH++Fw2pRXOzhSXElGfjkrD+aTkV9OoJ8PVw+K57aRCboAkOZgesCuui3N4pOtudz3/jamjUnk4fO7mY4j8h/mrs/itx/vYs6tZzEiKdp0HHFtput2farj0ihSj5UyfvoK7hmXxAPnJJuOIx5IzXZxIWq0i2d6f+MhHvloJ7+/sAe3juhsOo64kOo6B/PWH2LGqgxyTlQS4OdDSnwkCa3DaB8RhK+PRWWNg4z8crbnFJFz4uR+b5YFyW1a0L9jS/p3iKR/x5YkRIf+otlptm2z90gp767JZMHWHCzL4t5xSdw+KkErLaQpmR6wq25Lk8s5UcH5/1hJ9/YtmHfbEC1HF5dTXedg1N+W0jk6lPm3DzUdR1yb6bpdn+q4NIpHPtzBJ9tyWfPo2bQKCzQdRzzU98328GB/5t2mZrsYo0a7eJ5jJVWMm76cHu3DmXfbEHx8XO2aVVxBncPJioPHWZNawMasE+QUVlBQXgOcbKrHtQymT1wkfWIj6B0XQa/YCMKD/Bv8vEeKK3ly4R6+3HmU7u3DeeXaFBJahzX4cUV+hOkXP9VtaVJOp831M9ezPbuIr+8bpQGVuKwZK9P58xd7+WjqUAZ0jDIdR1yX6bpdn+q4NFheSRUj/7qUqwfF8+SlvUzHEQ+3I6eI62esJzz45Mz2uJa6NpRmp0a7eJ673tvC4j15fH3vSDUw5bRU1zkACPD1afJ9VBftPsojH+2g1mHz9yv6MKF3+yZ9PvFKpgfsqtvSpGavzeT/Pt3NU5N6c+1ZHUzHEflJFTV1DH9mCf3iI3nn5sGm44jrMl2361MdlwZ75qt9vLkijaUPjqFjq1DTccQLqNkuhjVqLddaXTHuu715fLHjCPec3UVNdjltgX6+BPr5NsthZef2bMfCe0bSpU0YU+du4eUlB2niNytFRDxGzokKnvlqH6OSWzN5cLzpOCL/U0iAH7cM78zS/cfZfbjYdBwRkWZRWlXL3HVZTOjdXk12aTZ94iKZM+UsiitruebNdWQXVpiOJHLG1GgXo6pqHfzhs90ktw3j9lGJpuOI/KzYyGDev2MIl/aL4dlFB3j8453UOZymY4mIuDTbtvnDp7uxbXhqUq9meXNUpKFuGNaJsEA/Xl2aZjqKiEizmLfhEKXVddwxKsF0FPEyfeIimTvlLEpONduzCspNRxI5I2q0i1Fvrkgn50Qlf7y4FwF++nEU9xDo58vzV/dj2phE5m3I5qEPd+Bwama7iMhP+Wb3Ub7bd4wHzknWcmBxGxHB/twwtCNf7jrC/qOlpuOIiDSpmjonM1dlMCyxFX3iIk3HES/UJy6S924bQnlNHVe/sY6MfDXbxf2osynGHC6q5NVlqUzs3Y6hia1MxxE5LZZl8fD53Xjw3GQ+3prLox/twKlmu4jIfymtquWJz/bQvX04Nw/vZDqOyGmZMjKBEH9f/vHtAdNRRESa1IItOeSVVHPHaK00F3N6xUYw77Yh1DicXP3GWlKPlZmOJHJa1GgXY57+ah+2DY9P7G46isgZu/vsJO4dl8S/Nufwp4V7tGe7iEg9zy06QF5pFU9N6oWfry49xb1EhQZw64jOfLXrqPZqFxGPVetw8sqyVPrGRTAqKdp0HPFy3duHM//2IThtuObNdaq/4lY02hEjNmQU8vn2w9wxOlFLyMXt3Tc+iVtHdGbWmkzeWJFuOo6IiMvYnl3Eu2sz+dWQjqR0aGk6jsgZuXVkAuFBfjy/+KDpKCIiTeKTrblkF1Zyz7gknaMiLiG5bQvm3z6EAF+Lq15fy7L9x0xHEvlF1GiXZudw2jzx2W5iIoKYqmVp4gEsy+K3E7tzUd8YnvlqH59uyzUdSUTEuDqHk8c/3knrsEAePK+r6TgiZywi2J/bRibw7d48tmcXmY4jItKo6hxOXlmaSs+YcM7u1sZ0HJEfdGkTxsd3DadTdCi3vruJeRsOmY4k8rPUaJdm9/7GbPYcKeGxid0JDvA1HUekUfj4WDx7ZR8Gd47i4Q93sCtXy9tExLvN25jN7sMl/N9FPQgP8jcdR6RBbh7RmZYh/kxfrL3aRcSzfLb9MJkFFZrNLi6pbXgQ798xlBFdonlswU7+/s0+bdcqLk2NdmlWZdV1PLtoP4M7R3Fhn/am44g0qkA/X169rj+tQgO4ffYm8suqTUcSETGiqKKG6Yv2MyQhigt6q96L+wsL9OOO0YksP3CczVmFpuOIiDQKh9Pm5SWpdGvXgnO6tzUdR+RHhQX6MfPGgUwe3IFXlqYxbe4WyqrrTMcS+VFqtEuzemdVBoXlNTw+sbveLRePFB0WyBu/GkhBeQ13zd1CrcNpOpKISLP7x7cHKa6s5Q8X9VS9F49xw9CORIcF8NwizWoXEc+wcMdh0vPLuWdcEj4+qtfiuvx8fXhqUi9+d0F3vtl9lEmvrCb9eJnpWCL/RY12aTbFFbW8uTKd8d3b0i8+0nQckSbTOy6CZy7vzfqMQv7yxV7TcUREmtXBvFL+uS6La8/qQPf24abjiDSakAA/po7pwpq0Alan5puOIyLSIE6nzUtLUklqE8b5PduZjiPysyzLYsrIBObcehYF5TVc8vJqvt2TZzqWyH9Qo12azYxV6ZRW1fHAOcmmo4g0uUkpcUwZ0ZlZazL5YFO26TgiIs3Ctm3+tHAPoQG+PHCODkAVz3PdWR2IjQzmL1/sxeHUHrEi4r6+2HmE1GNl/Fqz2cXNDOsSzee/HkGn6FCmzN7Es9/sp04rycVFqNEuzaKwvIa3V2UwsXc7esRodpt4h0cndGN4l1b8/pNd7D6sw1FFxPN9u/cYKw/mc/85yUSFBpiOI9Logvx9eWRCN/YcKWHBlhzTcUREzkhNnZNnF+0nuW2YzlIRtxQbGcy/7hzKVQPjeHlpKpPfWkduUaXpWCJqtEvzeGNFGhW1Du4br9ns4j38fH148ZoUWoYEMG3uFoora01HEhFpMtV1Dv78xR6S2oRx/ZCOpuOINJmL+rSnX3wkzy7aT0WNDmMTEfczZ10WWQUVPDaxO76azS5uKsjfl79d0Zd/XN2PPYdLmPjCSr7ZfdR0LPFyarRLkztWWsW7azK5pG8MyW1bmI4j0qxahQXyynUp5J6o5KF/bce2tcxcRDzTu2syySqo4PcX9sDfV5eY4rksy+L3F3Ynr6Sat1ZkmI4jInJaiitreXHJQYZ3acWY5Nam44g02KUpsXxxz0jio4K545+beeKz3dTUaSsZMUOjIGlyry1Lo9Zhc69ms4uXGtAxiscmdmfRnjzeWpluOo6ISKMrqqjh5SWpjO3amlEatIsXGNAxiom92/HGijSOlVSZjiMi8ou9uiyV4spaHp/YHcvSbHbxDJ2iQ/lo6jBuGX7ynLTrZqzjWKnqszQ/NdqlSR0trmLu+kNclhJL5+hQ03FEjLlleCcm9GrHX7/ez4aMQtNxREQa1UtLUimrruPRCd1NRxFpNo+c341ah5PnFh0wHUVE5BfJOVHBO6szmZQSS8+YCNNxRBpVoJ8v/3dRD16cnMLO3GIuemkVWw6dMB1LvIwa7dKkZqxMx+G0+fXZSaajiBhlWRZ/u6IPHaJCuPu9LXp3XUQ8xqGCCmavzeTKAfF0bact4sR7dGwVyg1DO/HB5mz2HikxHUdE5Gc9+81+LODBc7uajiLSZC7uG8OCqcMJ8PPhmjfWMX/DIdORxIuo0S5Npqiihvc2HOKiPu3p0CrEdBwR41oE+fPa9f0pqarl3nnbqHNo3zgRcX9/+2Yffj4+PHCutogT73PP2UlEBPvzxGe7dQ6LiLi0nTnFfLLtMLeO6ExMZLDpOCJNqkdMOJ/fPYKzEqJ4dMFOnv1mv+q0NAs12qXJvLsmi4oaB3eOSTQdRcRldGsXzp8v7c3a9AKmL9ZScxFxb9uyi1i44wi3jexM2/Ag03FEml1EiD8Pn9eN9RmFfLb9sOk4IiI/yrZtnly4h6jQAI3PxWtEhgQw6+bBTB4cz8tLU/ntJ7twONVsl6alRrs0iYqaOmatyWBctzZ0axduOo6IS7liQByTB8fz6rI0vtubZzqOiMgZsW2bp77YS3RYALeP1qBdvNfVg+LpExfBU1/upay6znQcEZH/8vHWXDZkFvLweV0JD/I3HUek2fj6WDw1qTfTxiTy3vpD/O6TnZrZLk1KjXZpEvM3ZHOiopaperdc5Ef94aKe9IwJ5/73t5FdWGE6jojIaVu8J48NmYXcNz6ZsEA/03FEjPH1sfjTJb04VlrNi98dNB1HROQ/FFfW8tSXe0npEMlVA+NNxxFpdpZl8fD53bhrbCLzNmTz1Jd71WyXJqNGuzS6mjonM1amM7hTFAM7RZmOI+KSgvx9ee26AdjA1Lmbqap1mI4kIvKL1TqcPPP1PhJbh3LNIA3aRfrFR3L1wHjeXpXBwbxS03FERH7w3KL9FJbX8OQlvfDxsUzHETHmwXO7cuPQjry1MoOXlqSajiMeSo12aXSfbT/M4eIqzWYX+RkdWoUw/ap+7Mot4cmFe0zHERH5xeZvzCb9eDmPTuiOn68uJ0UAHj6/G6GBfvzfpzoYVURcw+asQv65LosbhnaiV2yE6TgiRlmWxR8u6snl/eOYvvgAb6/KMB1JPJBGRtKonE6b15en0a1dC8Z0bW06jojLO6dHW+4cncjc9Yf4eGuO6TgiIj+rrLqOF749wODOUYzv3sZ0HBGXERUawIPndWVtegELdxwxHUdEvFx1nYNHPtpJTEQwD57X1XQcEZfg42Px18t7c37Pdvxp4R6NwaXRqdEujWrx3jxSj5UxdUwilqVlaSK/xIPnJjO4cxSPL9jF/qNabi4iru2N5Wnkl9Xw+MTuqvUi9Vw7uAO9YyP408I9FFfUmo4jIl7slSWppB4r4y+TeuksFZF/4+frwwuT+zEkIYpHPtrJ9uwi05HEg6jRLo1q5qoMYiODuaB3e9NRRNyGn68PL09OITTQj6lzN1NWXWc6kojIjzpcVMlbK9O5qG8M/eIjTccRcTm+PhbPXN775H7IX2hbOBExY+uhE7yyLI1JKbGM6arVZyL1Bfr58up1A2gdFsgd/9zMsdIq05HEQ6jRLo1mV24xGzIKuWlYJ+3XKnKa2oQH8fK1KWTml/PIRzu0t6uIuKRnv9mP04aHtQRd5Cf1jIngztEJfLg5h+UHjpuOIyJeprSqlnvnb6NdeBBPXNzTdBwRlxUVGsCbNwygqLKGe+ZtxeHUGFwaTt1QaTTvrM4kJMCXqwbFm44i4paGJLTiofO68cWOI7y1Mt10HBGR/7Ajp4gFW3O5dURn4qNCTMcRcWm/PjuJxNahPL5gp1aqiUiz+sNnu8k5UcE/rulHRLC/6TgiLq1nTAR/vrQ369ILeWnJQdNxxAOo0S6N4nhpNZ9vP8wVA+JUzEUa4M7RCVzQuz1Pf7WPb3YfNR1HRAQA27b588K9tAoNYNqYRNNxRFxekL8vf7uiD4eLK/n71/tMxxERLzF3fRYLtuTy67OTGNQpynQcEbdwxYA4LusfywvfHWRNWr7pOOLm1GiXRjF3fRY1Dic3DetkOoqIW7Msi+eu6kvfuEjum7+NnTnFpiOJiPDN7qNsyCzkgXOTaRGkN9RFfokBHaO4cWgn3l2bxcbMQtNxRMTDbcos5InPdjOma2vuGZdkOo6IW3nykl50jg7lNx9sp6RKh5nLmVOjXRqsus7BnHWHGNu1NQmtw0zHEXF7Qf6+vHXDQKJCA7j13Y0cKa40HUlEvFhNnZOnv9pHctswrh6o7eFETsdD53UlrmUwj3y4g6pah+k4IuKh8kqqmDp3CzGRwbxwdQq+PpbpSCJuJTTQj+lX9SOvpIq/LNxrOo64MTXapcEWbj9Cflk1Nw/vbDqKiMdo3SKQmTcNpKLGwa2zNlGu/V1FxJDZazPJKqjgtxf00GHnIqcpNNCPpy/rTXp+OS98p71fRaTxVdc5uHPOZsqr63jrhoFEhGjlmciZ6BcfyR2jE3l/UzbL9h8zHUfclEZL0iC2bfP26gy6tAljZFK06TgiHqVbu3BevjaFfUdLuOu9LdQ6nKYjyf9j777Dq6jyP45/TnpCOiUJAUJLgNB7tyMoFhRERSygYi+oq65tXdfuujZUEBdRQUQRxYYNG723UBNKqCEJIZ3UO78/kt3F/EAgbW55v54nDw/JMHzOuTdzZr535hzAw2TmF+v1Bck6M6GxzkxobHccwCUNjm+s0b2a6Z3fdyppP1PCAag9lmXpiS82ae2ebP1rdFclRIXYHQlwafeeF6/4JsF6+LONyjnKFDI4fRTaUSMrdx/RpgO5GjewpYzh8TSgtp3VromeHtFZv27L0EOfbZBlWXZHAuBBnp+/VUdLy/XExYl2RwFc2qPDE9WwgZ/+MmeDSsr44BxA7Zi6cKdmr9qrO89uq2GdYuyOA7g8fx9v/fOKrsrIL9bTX2+2Ow5cEIV21Mh7i3cpLNBXl3dvZncUwG2N6dtC9w1J0Nw1+/X8d1vt49X62AAAIABJREFUjgPAQ6zanaU5q/fp5sGt1YY1WIAaCQv01TOXddaWg7l6bcF2u+MAcAOfrd6nZ7/dquFdYjRxSILdcQC30bV5uG49s7U+Xb1PP289ZHccuBgK7ai2vVmF+n5Tmq7u00KBft52xwHc2l3ntNW1/eI05bedenfhTrvjAHBzZeUOPfZFkpqGBejOc9raHQdwC0MSo3RFz2Z6+9cdWp2aZXccAC7s562H9OBnGzSwbUP9a3RXFj8Fatnd58arXVSI/jp3o/KKmEIGp45CO6rtw2WpMsbouv5xdkcB3J4xRk9e0lEXdo7W099s0edr99kdCYAb+3BZqram5emJixMV5OdjdxzAbTxxcaKahgfqvk/Ws9A5gGpZnZql22euUWJMqKZc20v+Ptz0BtQ2fx9vvTCqi9LzivXyDzyJhlNHoR3VUlBcpo9X7NGwTtFqGh5odxzAI3h7Gb1yZTf1b91QD3y6Qd8lpdkdCYAbSs8t0r9+2K4zEhpraMdou+MAbiUkwFf/Gt1Ne7IK9fQ3W+yOA8DFbD+Up/HTVykmLFDvjeutYH8+DAfqSrfm4RrbN04fLN2tjftYzBynhkI7qmXumn3KLSrT+IEt7Y4CeBR/H29Nvb6XujQL012z1uiXrel2RwLgZp6bv1XFZQ79/ZKOLHQO1IE+rSI1YXBrzVqxh7lfAZyy7YfydPU7y+Tv46UPxvdRo2B/uyMBbu+Boe3UMNhfj36xUeUOy+44cAEU2nHaHA5L7y3Zra7NwtSjRYTdcQCPE+zvo+nj+qhddIhumbFai1My7Y4EwE0sSs7U52v3a8IZrdWqUQO74wBu677zE9Q+OkQPztmorIISu+MAcHLb0iqK7N5eRrMm9FPzyCC7IwEeISzQV49flKgN+3I0Y1mq3XHgAii047T9lpyhnRkFGjewFXe6ATYJC/TVh+P7qnWjBrrp/VVasYtF1QDUTH5xmR76bINaN27AAqhAHfP38dYrV3ZT7tFSPTJ3oyyLu+QAHN/6vdm68p2l8vYy+nhCP7VpHGx3JMCjXNwlRoPjG+ml77fpUG6R3XHg5Ci047S9t3i3moT468LOMXZHATxaRAM/fXhjXzUND9D46Su1OpViO4Dqe/bbLTqQc1QvjeqqAF8WVgPqWoeYUN1/foK+25SmuWv22x0HgBNauuOwxkxdppAAH825dYBaU2QH6p0xRv+4tJNKyh166uvNdseBk6PQjtOSkp6n37dn6Np+cfLz4e0D2K1xiL9m3tRPjUP8df00iu0Aque37Rn6aPke3TSolXrGMS0cUF9uGtxafVpG6skvN2nfkUK74wBwIgu2HNL1761Q0/BAfXrLALVoyHQxgF1aNmqgO89uq282HNSv21gnDSdGpRSn5b3Fu+Xn46UxfVvYHQVApeiwAM26mWI7gOrJyCvW/Z+sU0JUsO4/v53dcQCP4u1l9PLorrIkTZy9joXWAEiS5q3br1s+XK320SGafUt/RYcF2B0J8Hi3nNlarRs30BPzNqmotNzuOHBSFNpxyrILSzR3zX5d2rWpGrLCOeBUKLYDqA6Hw9J9n6xTXlGZJo3pwZQxgA2aRwbp6RGdtHL3EU36OcXuOABsNn3xLt07e516xkVo5k19FdnAz+5IAFSxvsrTIzppT1Yh4zVOiEI7TtmsFXt1tLRc4we1sjsKgOOg2A7gdL35S4oWJmfqiYsTlRAVYnccwGON6B6ry7vH6rUF27VqN+M34Iksy9LLP2zTk19t1nkdovT++D4KCfC1OxaAYwxo00iXd4/VlN93KCU9z+44cEIU2nFKSssd+mDpbg1o01AdYkLtjgPgBCi2AzhVP2xK08s/bteIbk01pg9TwgF2+/ulHdUsIkj3fLxOOUdL7Y4DoB6VOyw98nmS3vg5RaN7NdPb1/CUGeCsHhneQUF+Pnrk8yRZFlO+4Y8otOOUzE9K08GcIt3I3eyA06PYDuBktqblauLsderaLEzPj+wiY4zdkQCPFxLgq9ev7q5DuUV69PONXLwDHqKotFx3zFyjWSv26Paz2uiFkV3k402pBnBWjYL99fAF7bViV5bmrN5ndxw4GY7eOCXTFu1Sy4ZBOrtdE7ujADgFFNsBnEhaTpHGvbdSDfx9NOXaXtwxBziRbs3DNXFIgr7ecJCLd8AD5BWV6ob3Vui7TWl6/KJEPTisPR9+Ay7gyl7N1TMuQs9+u0VHCkrsjgMnQqEdJ7VmzxGt25utcQNbycuLQR9wFRTbAVSVV1SqcdNXKvdoqd4b11vRYQF2RwJQxa1ntlG/1pH625ebtCuzwO44AOpIRl6xrnpnmVbtPqJXr+zG0+OAC/HyMnrmsk7KKyrTc/O32B0HToRCO05q2qJdCgnw0aiezeyOAuA0UWwH8B95RaW6cfoqbT+Up7fG9lTHpmF2RwJwHN5eRq9c2U1+Pl66e9ZalZQ57I4EoJbtOVyoUZOXaGdGgaZe30sjusfaHQnAaWofHaobB7fSJ6v2acUurrNRgUI7/tT+7KOan5Smq3o3VwN/H7vjAKgGiu0AsgtLNPbd5Vqz54heu6qbzkxobHckAH8iJixQL4zsoo37c/Tyj9vsjgOgFm0+kKuRk5co52ipZt7cl+lZARd2z7nxig0P1KOfb+SDcUii0I6T+GDpblmWpesHtLQ7CoAaoNgOeK69WYW6YvJSbTmYp8lje+qiLk3tjgTgFAztGK1r+rbQlN92alFypt1xANSC5TsP68opS+XjZfTpLf3Vo0WE3ZEA1ECQn4+eurSjktPz9e6inXbHgROg0I4TKiwp06zlezSsU7SaRQTZHQdADVFsBzzP2j1HdNlbi3Uot0jTx/fWeYlRdkcCcBoeG56o+CbBuu+TdTqcX2x3HAA1sGDLIV03bYWahPprzm0DFB8VYnckALXg3A5RGtoxSq8vSNberEK748BmFNpxQrNW7FVuURmLsgBuhGI74BnKHZbe/nWHRk9ZqkA/b829faAGtGlkdywApynQz1uvX91d2UdL9dBnG2RZlt2RAFTDNxsO6pYPV6tddIg+vXWAYsMD7Y4EoBY9eUlHeRujx+clMVZ7OArtOK6SMofeXbhTfVpFqmdcpN1xANQiiu2Ae9t+KE9XT12mF77bqvM6ROnLOwapbZNgu2MBqKYOMaH66wXt9dOWdH24LNXuOABO02er9+muWWvUvUW4Zt7UV5EN/OyOBKCWxYQFauKQBP26LUPfbkyzOw5sRKEdx/XFuv06mFOk285qY3cUAHWAYjvgfrIKSvTUV5t1wWsLtS0tTy+N6qK3rumhCC7oAZd3w4CWOrtdYz39zRZtS8uzOw6AUzRzearu/3S9BrRppPfH91FIgK/dkQDUkRsGtFSn2FA9MS9JmUz35rFMHT/SwPMSLqjcYWnIK78pwMdb39w9SMYYuyMBqCNpOUW6euoyZeQV6/3xvXmCxX52H3AZt13QwZyj+nBpqt5fsluFpeW6qncL/WVoO+6YA9xMZn6xhr26UJENfPXlnYMU4OttdyTYP25XxTjuRN5duFNPf7NF57Zvojev6cHvLOABtqXl6eI3Funs9o01eWxP6mmuoVZfJO5ox//zw6Y07cwo0G1nteGgALi5qne2b9iXbXckAKegtNyh7zelafz0lRr4/M9669cdOrt9E/048Qw9d3lniuyAG2oU7K9Xruyq7Yfy9Y+vN9sdB8CfmPRzsp7+ZouGd47R22N7UmQHPES76BDdf36Cvt90SJ+v3W93HNiAO9rxB5Zl6dI3FyvnaKl+vv8seXtRaAc8QVpOkUZNXqKC4jJ9ckt/xUeF2B3JU9l90GXcdmIlZQ4t33VYP20+pG+T0pSRV6wmIf4a3au5RvdqrhYNg+yOCKAePDd/i6b8tlNvX9NDF3SOsTuOp7N73K6KcdxmlmXppe+36a1fd+jy7rF6cVQX+XhzfyPgScodlq56Z6m2puXp+3vPUFMWP3Z2tTqWU2jHHyxKztTYfy/Xs5d11pi+LeyOA6AepR4u0KjJS+VlpDm3DlDzSIp2NrD7gp1x28nkFZXq120Z+mHzIf26NV15xWUK8PXSGfGNdUWv5jq7XWMu4AEPU1ru0KjJS7UrI1/f3jNYzSIYr21k97hdFeO4jSzL0t+/2qzpS3br6j4t9MyITvLixjXAI6UeLtAFry1Uz7gIfTC+D7NFODcK7ag717y7TMmH8rXwobPl78PjbYCn2ZaWp9FTlios0Fef3tpfUaEBdkfyNHafgTFuO4Gi0nL9uLnicdOFyRkqLbfUsIGfzusQpSGJURoU34hH0AEPt+dwoYa/vlAJ0SGaPaEfH7jZx+5xuyrGcZuUOyw99sVGzVqxV+MHttLjF3WgsAZ4uBnLUvXYF0l66tKOuq5/S7vj4MQotKNurNubrRFvLtYjF7bXhDPa2B0HgE3W7c3WNVOXKTYiULMn9FcEcz3XJ7uvyBi3bWJZllbuPqI5q/dq/sY05RWXKSYsQBd1idHQjtHq3iKC6dwA/MGX6w/o7llrdefZbfXA0HZ2x/FUznZgZhy3QVm5Qw98ul5frDugO89uq/vPT6DIDkCWZemG91Zq2c7DmnfnQLWPDrU7Eo6PQjvqxnXTVmjjvmwtfOgcBfv72B0HgI2W7MjUDe+tVIfoEM28uR/HhPpj91UZ43Y9syxL329K09u/7tD6fTlq4OetYZ1idHmPWPVr3ZDiOoA/9eCc9fp09T7NvLGvBrRtZHccT+RsB2nG8XpWUubQPR+v1fykNP1laDvdcXZbuyMBcCIZecW68PWFCgnw0Vd3DlIDrqudEYV21L5lOw/rqneW6dELO+jmM1rbHQeAE/hx8yHdOmO1ereM0PRxfZiqon7YfcHOuF2P1u/N1lNfb9bq1COKaxikmwe31uU9YhXkxwk4gFNTWFKmi99YpLyiMs2/Z7AaBvvbHcnT2D1uV8U4Xo+KSst124zV+mVbhh6/KFE3DmpldyQATmjJjkyNfXe5LunaVK9c2Y0nXpxPrb4gTOYHWZalf36/TVGh/rq2f5zdcQA4iSGJUXr5iq5avitL93y8VuUOrt2A2lBUWq6nvtqsEW8tVurhAj1/eWf9fP9ZGtsvjiI7gNMS5OejN67uoeyjpfrLnA2q45uoAFQqKC7T+Okr9ev2DD17WWeK7ABOaECbRrr3vAR9se6AZq/ca3cc1DEK7dCv2zK0KvWI7jonnjtWAfzBiO6xemx4or7fdEhPfrmJC3ighrYczNWFry/UtMW7dG2/OP3ywFm6qk8LpogBUG2JTUP16IUd9PPWdE1bvNvuOIDbyy0q1XXTVmjZzsP61+iuGtO3hd2RADi5O85uq0FtG+lvX27SloO5dsdBHaLQ7uEcDkv//GGbWkQGaXSv5nbHAeCEbhzUShPOaK0Pl6XqrV932B0HcFnfJaVp5NtLVFBcphk39tVTl3ZSSICv3bEAuIHr+sdpSGKUnp+/RUn7c+yOA7itIwUlGvvucq3fm61JY3rosu7N7I4EwAV4exm9cmU3hQb66o6Za5RXVGp3JNQRCu0ebn5SmjYdyNXEIfHy8+HtAOD4Hh7WXiO6NdVL32/Tp6t43A04XVN+26FbZ6xWQlSIvrpzkAbFs2ghgNpjjNGLI7uoYQN/3TVrrfKLy+yOBLidtJwijZ6yVFvT8jTl2p66sHOM3ZEAuJDGIf56/aruSs0q1MTZ6+Vgala3RGXVg5WVO/Tyj9uUEBWsS7rG2h0HgBPz8jJ6cVRXDY5vpIfnbtSv29LtjgS4BIfD0rPfbtFz87fq4q5N9fGEfmoSGmB3LABuKKKBn169qptSDxfoiXlJdscB3Erq4QKNmrxEB7KP6v1xfXRuhyi7IwFwQf3bNNRjwzvopy2H9NqCZLvjoA5QaPdgc9fs186MAt03pB1zwwI4KT8fL709tqfaRYXoro/WKvlQnt2RAKdmWZYen5ekd37fqev7x+m1K7uxFgqAOtWvdUPdeU685q7Zr8/X7rM7DuAWthzM1ajJS1VQXKZZE/qpf5uGdkcC4MJuGNBSI3s002sLkvVdUprdcVDLKLR7qPziMr30wzZ1bR6uoR35NB7AqQn299G71/eSv6+3bnx/lY4UlNgdCXBaL/+wXTOX79EtZ7bWk5d0lBcfagOoB3ef01Z9Wkbqsc+TtCuzwO44gEtbnZqlK6cslbcx+uSW/urSLNzuSABcnDFGz1zWSV2bhen+T9ZpOzewuRUK7R5q0s8pysgr1pMXJ8oYLvwBnLqm4YF657qeSsst0m0zV6u03GF3JMDpTF+8S5N+SdFVvZvr4WHtGWsB1Bsfby+9elU3+Xh76e5Za1VSxjgNVMc3Gw7q6qnL1TDYX5/e2l/xUSF2RwLgJgJ8vTX52p4K9PPRzR+sUnYhN7C5CwrtHmh3ZoGmLdqly3vEqnuLCLvjAHBBPVpE6IWRnbVsZ5b+9uUmWRYLuQD/8cvWdD319WadnxilZy7rTJEdQL1rGh6oF0d10cb9OXrxu612xwFcimVZmvzbDt3x0Rp1iQ3T3NsGqHlkkN2xALiZmLBATR7bQweyj+qWD1eruKzc7kioBRTaPYxlWfr7V5vk62308LD2dscB4MIu695Mt53VRh8t36MPlqbaHQdwCtvS8nTXrLXqEBOqV6/qxhooAGwztGO0ru0Xp3cX7dIvW1nEHDgVZeUOPfZFkp6fv1XDu8Roxk19FdHAz+5YANxUr5aRemlUVy3flaW/fraRG9jcAIV2D/PNxoP6ZVuGJg5JUJPQALvjAHBxfzm/nc7r0ERPfb1ZC5Mz7I4D2Cq3qFQ3f7BKgX7eevf6Xgry87E7EgAP9+jwDmofHaL7PlmnA9lH7Y4DOLXcolLd+P4qzVy+R7ee2UZvXNWdRcwB1LkR3WN1/5AEzV27X6/+lGx3HNQQhXYPklNYqie/3KzOsWG6YUBLu+MAcANeXkavXtVd8U2CdcfMNdqZkW93JMAWlmXpwU836ED2UU0e20MxYYF2RwIABfh6661reqi03NIdH61hvnbgBHZnFuiyNxdrcUqmnr2ssx6+oD2LmAOoN3ee01ajejbTawuSNWf1PrvjoAYotHuQZ7/doiOFJXru8s7y8ealB1A7gv19NPW6XvLx9tJN769STmGp3ZGAevfe4t36blOaHhrWXj3jIu2OAwD/1bpxsF4Y2UVr92Truflb7I4DOJ0lKZm69M3FOlxQog9v7KsxfVvYHQmAhzHG6NnLOmtg24Z6+LMNWpKSaXckVBPVVg/xy9Z0zV61VzcNbqVOsWF2xwHgZppHBmnKtT2190ih7py1RmXl3DEHz7HpQI6em79F53WI0k2DW9kdBwD+n+FdYjRuYEu9t3i3vt140O44gFOwLEsfLkvVddNWqEmIv+bdMVD92zS0OxYAD+Xn46W3rumpVo0a6JYZq5V8KM/uSKgGCu0e4EhBiR78bIPaRYXoviEJdscB4KZ6t4zUMyM6a2Fypp7+hjvm4BmKSss1cfY6hQf56cVRXWQMj5kDcE5/vaCDurcI14NzNjDVGzxeYUmZ7vtkvR7/IkmD4xtp7u0DFNewgd2xAHi4sEBfvTeutwJ8vXXDeyuVnldkdyScJgrtbs6yLD32RZKyC0v0ryu7yt+HxVwA1J3RvZvrpkGtNH3Jbs1eucfuOECde+n7bdp+KF8vjeqiyAZ+dscBgBPy8/HSpDE95OttdPvMNTpaUm53JMAWKel5GvHmYn2xbr8mnpegd6/vrZAAX7tjAYAkqVlEkP59fS9lFZRo/PSVyi8uszsSTgOFdjc3Y1mqvtl4UBOHJKhjU6aMAVD3Hr6gvQbHN9JjXyRpdWqW3XGAOrNqd5amLd6lsf1a6Kx2TeyOAwAnFRseqFeu7KZth/L0xLwku+MA9W7euv26ZNJiHc4v0Qfj++ie8+LlzaKnAJxMl2bhevOa7tpyME+3fLhKxWV8OO4qKLS7sXV7s/XU15t1drvGuvWMNnbHAeAhfLy9NOnqHmoaHqhbZ6xRWg6Pu8H9FJeV6+G5G9U0LFB/vaCD3XEA4JSd1a6J7jq7rT5dvU+frNxrdxygXhSXleuxLzbqno/XKTEmVN/cPViD4xvbHQsATuic9lF6cWQXLU45rPs+Wa9yh2V3JJwCCu1uKj2vSHfMXKMmIQF65cpu8uJTegD1KCzIV1Ov66XC4jLd8uEqFZXyCTzcy1u/7FBKer6evqyTGvj72B0HAE7LPeclaGDbhnp8XpLW7822Ow5Qp3ZnFuiKyUs1Y9keTTijtWZN6KfosAC7YwHASY3s2UyPXNhe32w4qL9/tUmWRbHd2VFod0MFxWUaP32lsgpKNHlsT4UHMWcsgPqXEBWiV67spvX7cvTo50mcFMBtJB/K01u/pujSbk11NlPGAHBB3l5Gr1/VXY2C/TXhw1VKz+XpM7gfy7L0ycq9uvD1hdqdWaAp1/bUIxd2kK83ZRAArmPCGW004YzW+mBpqt74OcXuODgJRhg3U1ru0O0z12jLwTy9dU0PdW7GvOwA7HN+x2jde168PluzT9MW77Y7DlBjDoelhz7boGB/Hz1xUaLdcQCg2hoG++vd63spr6hMEz5czdNncCtHCkp024w1evCzDerSLEzf3XuGhnaMtjsWAFTLw8Pa6/IesfrXj9s1Y1mq3XHwJyi0uxHLsvTY50n6bXuGnh7RSWe35y47APa7+5x4De0YpWe/3aJFyZl2xwFqZMbyVK3Zk63HhieqYbC/3XEAoEY6xITqX6O7at3ebD0ydyNPn8EtLErO1LDXfteCrYf01wva66Ob+qlpeKDdsQCg2ry8jF4Y2UXntG+ix+claf7Gg3ZHwglQaHcjry9I0exVe3X3OW11dZ8WdscBAEkVJwUvj+6mNo0b6M5Za7TncKHdkYBqOZB9VC9+t02D4xvp8h6xdscBgFoxrFOMJp6XoLlr92vqwp12xwGqrai0XP/4erPG/nu5QgJ89fntA3XLmW1YrwyAW/D19tKbY3qoR4sI3fPxOi3ZwU1szohCu5t4f8luvfLTdo3s0UwThyTYHQcA/iDY30dTr+sly5Ju/mCVCorL7I4EnBbLsvTEvCSVORx6ZkRnGcNFOwD3cdc5bXVh52g9N3+rftx8yO44wGnbfihPI95crH8v2qXr+sfpqzsHqVMs06gCcC+Bft769/W91LJRkCZ8sFpJ+3PsjoQqKLS7gemLd+lvX27S+YlRen4kF/8AnFNcwwaaNKa7ktPz9MCn6+Vw8Hg6XMe3G9P005Z03T+knVo0DLI7DgDUKi8vo39e0VVdYsN016w1WrvniN2RgFNS7rD070W7dNEbi5SZX6xpN/TSU5d2UqCft93RAKBOhAf56f3xfRQa4KMb3lup1MMFdkfCMUwdz8NHFaUOWZalt3/boRe/26ahHaM0aUwPVlAH4PTeXbhTT3+zRfcNSdDd58bbHcfZ2P1JKeP2cWQXlui8f/2mmLBAfX77APkw1gJwUxl5xRr59hIVFJfp01v7q3XjYLsjOTu7x+2qPGocT0nP04NzNmjNnmyd276JXhjVRY1YPwWAh0hJz9cVk5coJMBXc27rryYhAXZHclW1OpZzpeiiysodeuyLJL343TZd0rUpRXYALuPGQa10efeKFdN5PB2u4Nlvt+hIYameH9mZIjsAt9Y4xF/Tx/WWJI2Zupy75OCUSssdevOXFF342iLtzCzQq1d207vX96LIDsCjtG0SrGk39FZGXrFumLZSuUWldkeCuKPdJRWWlOmuj9ZqwdZ03XZWG/3l/HYs8ALApRSVlmv0lKXamVGgz28foPioELsjOQu7D+aM21UsScnUmHeX69Yz2+jhC9rbHQcA6sXWtFxd/c4yBfp66+MJ/Zky68TsHrercvtxfNOBHD04Z4M2HcjV8M4xevKSjmocQoEdgOf6bXuGbpy+Uj3jIvT++D4K8GXqrNNUq2M5hXYXk5FXrBvfX6mk/Tl66tJOGtsvzu5IAFAtB3OO6uI3FivY31vz7hiksCBfuyM5A7sv2Bm3j1FUWq6hr/4uSfr+3jM4aQXgUTYdyNE17y6Xn7eXZtzUVwl8KH48do/bVbntOF5cVq43FqRo8m87FB7kp6dHdNSwTjF2xwIAp/DF2v26d/Y6DesYrTev6SFvbsY9HUwd46lW7MrSRW8sVPKhfE29rhdFdgAuLSYsUJPH9tD+7KO66+O1KmdxVDiZV39KVurhQj13WWeK7AA8TsemYZo9ob8kafSUpVrDAqmwyZo9R3TR64s06ZcUXdotVj/ddwZFdgA4xojusXriokR9tylND87ZIAfX1rah0O4CHA5Lb/6SoqunLlOQn48+u22Azu0QZXcsAKixXi0j9dSlnfT79gy9+N1Wu+MA/7XpQI6mLtyp0b2aaUDbRnbHAQBbtIsO0ZxbByg0wFdXvbNMX60/YHekU1ZUWq5DuUXampar22as1vjpK5VfXGZ3LJyGrIIS/XXuxv8u0PveuN56eXRXhQf52R0NAJzO+EGtdN+QBH22Zp/+OncjxXab+NgdAH8uq6BEE2ev02/bM3RRlxg9d3lnhQQwvQIA93F1nxbadCBHU37fqcSmobq0W6zdkeDhysodevizjYoI8tMjF3awOw4A2KpFwyB9fvsA3Tpjte6atVZbDubq3vMS5Odj7z1bpeUOpR4u0PZD+dqWlqddmQVKzSrUoZwiZR8tUVGp4//9m9TDBerYNMyGtDgd5Q5LHy1P1T9/2K784jKNG9BKE4fEcx0MACdx97nxKit36PWfU+TjbfT0iE4yhmlk6hOFdie2cneW7vporbIKS/T0iE66pm8LfkEAuKUnLuqo7Yfy9eCcDWrdKFidm3ERDPtMW7xLG/fnaNKY7tw1BwCSGgb7a8ZNffW3eZv01q879Ou2DL08uqs6xITWW4bD+cVasStLy3dlaeXuLCUfyldFZQ21AAAgAElEQVRJeUUx3RgpNjxQLRs2UEJ8I0U08FNYoK/Cg3wVHuinST8ny8fbqE3j4HrLi+pZuTtLT8zbpC0Hc9W/dUP9/dKOrA8AAKdh4pAElTosvf3rDvl4GT15SUdqifWIxVCdULnD0pTfd+jlH7areUSgJo3poU6xFJ0AuLfM/GJd8sYiOSxp7u0D1DQ80O5IdrD7DMjjx+2U9Dxd+PoinZnQWO9c25OTUgCo4odNaXrk8406Uliqa/vFaeJ5CXWyoPmh3CIt35Wl5TsPa/muLKWk50uSAny91DMuQp1iw9QuKkQJUSFq0zhYgX4nXkvjyilLJUmzb+lf2zGdbZBw2XE8LadIL3y3VZ+v3a+YsAA9NjxRF3aOZhwGgGqwLEvPfrtFUxfu0viBrfT4RR04np5YrXYMhXYnszuzQA98ul6rUo9oeOcYPT+SqWIAeI4tB3M1evJSRYcFaM6tA+rkwt3J2X3249Hjdlm5Q5e/vUR7swr1w8Qz1TjE3+5IAOCUjhSU6OUft+mj5XvUwN9HY/q20LgBrRQdFlDtfe47UqjlO7O0fNdhrdiVpd2HCyVJwf4+6tUyQn1bNVSfVpHqHBt22tPWUGh3XvnFZXrntx16Z+FOORzShDNa6/az2yjIj4fvAaAmLMvSU19v1nuLd+uWM1rr4QvaU2w/Pgrt7sjhsDRjeaqe+3arfLyNnrq0o0Z0i+WXAIDHWbIjUzdMW6luzcP1wY19FOB74jvU3JDdB32PHrffWJCsl3/crjfH9NDwLjF2xwEAp7f5QK7e/CVF85MOypLUKy5C53WIUo+4CHVsGnrcYunRknLtyixQSka+UtLzlZKep/V7c7Q/+6gkKSzQV31aRapvq0j1bdVQHWJC5ONds/ngKbQ7n7Jyh2av2qtXfkxWZn6xLuoSoweHtleLhkF2RwMAt2FZlp6Yt0kfLkvVrWe20UPD2lFn/P8otLub/dlH9eCc9VqcclhnJDTWCyM7KybMI6dMAABJ0lfrD+iuWWt1QadoTRrTQ95eHnMyYHdDPXbc3nQgRyPeXKxhnWL0xtXd7Y4DAC5lz+FCfbZmn77flKataXn//X54kK8iG/jJSCott3SkoER5xWX//bmXkeIaNlCHmBD1bdVQfVtHKqFJiLxqedyn0O48yh2Wvlp/QK8vSNbOzAL1bhmhRy7soO4tIuyOBgBuyeGw9Ni8JH20fI9uGNBSf7s4kWL7H9VqZ/A8lo0sy9Knq/bpqa83y7IsPXd5Z13VuzlveAAe7+KuTXUot0hPf7NFT321iQVcUKeKy8p1/yfrFR7kp6cu6Wh3HABwOS0aBmnikARNHJKg9Nwibdyfoy0Hc3Uot1iHC4pljJGvl1F4kJ8ah/irRWSQ4qOC1bJhA097cs1jlTssfb3hgF5bkKydGQVqHx2iKdf21PmJUZzjAUAd8vIyemZEJwX4eGva4l0qLivX0yM6e9LNbPWKQrtNDuUW6eHPNuiXbRnq1zpSL43qquaRPCYHAP9x0+DWOpRbpKkLdynQz4fH3FBnXvspWVvT8vTv63spooGf3XEAwKU1CQ3QuaEBOrdDlN1R4ATKHZa+2XhQry9IVkp6vtpFhejta3poaMfoWn9yAQBwfMYYPX5RBwX5eWvSLykqKnXopVFdajw1G/4/Cu31zLIsfbn+gJ6Yt0nFZeX628WJur5/S04yAOA4HrmwgwpLyjX5tx3y8TK6//wEiu2oVYtTMvX2bzs0ulczikIAANSSw/nF+nztfs1asUc7MgqUEBWsN8f00AWdKLADgB2MMXpgaDsF+Hrpnz9s19GScr16VTeeLKtlFNrr0eH8Yj32RZLmJ6Wpe4twvXxFV7VuHGx3LABwWsYY/ePSTip3WJr0S4p8vI3uPS/B7lhwE+l5Rbrn43Vq0zhYTzJlDAAANVJa7tCi5Ex9smqvftpySKXllro2D9cbV3fX8M4xFNgBwAnceU68gvx89NTXm3Xtv5dr6nW9FB7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\n",
      "text/plain": [
       "<Figure size 1490.4x993.6 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ms=2 \n",
    "# x = range(len(fit.extract()['u'][-1,:,0]))\n",
    "\n",
    "DF = df = pd.DataFrame(columns=['date','cases','recovered','deaths'])\n",
    "\n",
    "dates_all = dfc.columns[4:].values[:-1]\n",
    "\n",
    "dates = dates_all\n",
    "\n",
    "for i in range(len(dates)):\n",
    "  DF.loc[i] = pd.Series({'date':dates[i],\n",
    "                         'cases':dfc2[dates[i]].values[0] - (dfr2[dates[i]].values[0] + dfd2[dates[i]].values[0]),\n",
    "                         'recovered':dfr2[dates[i]].values[0],\n",
    "                         'deaths':dfd2[dates[i]].values[0]})\n",
    "\n",
    "x = range(len(dates[t0:]))\n",
    "\n",
    "print(len(x))\n",
    "print(np.shape(DF))\n",
    "\n",
    "if model.name in [\"sir1\",\"sir2\"]:\n",
    "    plt.plot(x,DF[\"cases\"][t0:],'bo', label=\"cases\",ms=ms)\n",
    "    plt.plot(x,DF[\"recovered\"][t0:] + DF[\"deaths\"][t0:],'o',color='orange',label=\"unknown\",ms=ms)\n",
    "    labels = [\"S\",\"I\",\"U\",\"Z\"]\n",
    "    c_ = [\"g\",\"b\",\"orange\",\"r\"]\n",
    "    Sind = 0\n",
    "    n = 3\n",
    "\n",
    "if model.name in [\"sicu\",\"sicuf\",\"sicuq\"]:\n",
    "    plt.plot(x,DF[\"cases\"][t0:],'bo', label=\"cases\",ms=ms)\n",
    "    plt.plot(x,DF[\"recovered\"][t0:] + DF[\"deaths\"][t0:],'o',color='orange',label=\"unknown\",ms=ms)\n",
    "    labels = ['C','U','I','S','Z']\n",
    "    c_ = ['b','orange','r','g','m']\n",
    "    n = 4\n",
    "\n",
    "if model.name in [\"sicrq\",\"sicrqm\"]:\n",
    "    plt.plot(x,DF[\"cases\"][t0:],'bo', label=\"cases\",ms=ms)\n",
    "    plt.plot(x,DF[\"recovered\"][t0:],'o',color='orange',label=\"unknown\",ms=ms)\n",
    "    plt.plot(x,DF[\"deaths\"][t0:],'x',color='k',label=\"unknown\",ms=ms)\n",
    "    labels = ['C','R','D','I','S','Z']\n",
    "    c_ = ['b','orange','k','r','g','m']\n",
    "    Sind = 4\n",
    "    n = 5   \n",
    "    \n",
    "lw=4\n",
    "a = 0.5\n",
    "for i in range(n):\n",
    "    plt.plot(model.stan_data['n_scale']*fit.extract()['u_predict'][-1,:,i],label=labels[i],lw=lw,alpha=a,color=c_[i])\n",
    "plt.plot(model.stan_data['n_scale']*(1-fit.extract()['u_predict'][-1,:,Sind]),label=labels[-1],lw=lw,alpha=a,color=c_[-1])\n",
    "plt.legend()\n",
    "plt.ylim((0,1000000))\n",
    "\n",
    "# lbC = []\n",
    "# ubC = []\n",
    "# lbR = []\n",
    "# ubR = []\n",
    "# lbD = []\n",
    "# ubD = []\n",
    "# for i in range(1,47):\n",
    "#     print(i)\n",
    "#     x = fit.stansummary(pars=[f'u[{i},1]'], probs=(0.25,0.975), digits_summary=2).split('\\n')[5]\n",
    "# #     print(x.split('\\t'))\n",
    "#     lbC.append(model.stan_data['n_scale']*float(x.split(' ')[5]))\n",
    "#     ubC.append(model.stan_data['n_scale']*float(x.split(' ')[6]))\n",
    "#     x = fit.stansummary(pars=[f'u[{i},2]'], probs=(0.25,0.975), digits_summary=2).split('\\n')[5]\n",
    "# #     print(x.split(' '))\n",
    "#     lbR.append(model.stan_data['n_scale']*float(x.split(' ')[3]))\n",
    "#     ubR.append(model.stan_data['n_scale']*float(x.split(' ')[4]))\n",
    "#     x = fit.stansummary(pars=[f'u[{i},4]'], probs=(0.25,0.975), digits_summary=2).split('\\n')[5]\n",
    "# #     print(x.split(' '))\n",
    "# #     lbD.append(model.stan_data['n_scale']*float(x.split(' ')[5]))\n",
    "# #     ubD.append(model.stan_data['n_scale']*float(x.split(' ')[6]))\n",
    "\n",
    "# plt.plot(lbC)\n",
    "# plt.plot(ubC)\n",
    "# plt.plot(lbR)\n",
    "# plt.plot(ubR)\n",
    "\n",
    "# labels = ['C','D','R','I','S','Z']\n",
    "# lw=4\n",
    "# a = 0.5\n",
    "# for i in range(5):\n",
    "#     plt.plot(model.stan_data['n_scale']*fit.extract()['u'][-1,:,i],label=labels[i],lw=lw,alpha=a)\n",
    "# plt.plot(model.stan_data['n_scale']*(1-fit.extract()['u'][-1,:,4]),label=labels[-1],lw=lw,alpha=a)\n",
    "# plt.legend()\n",
    "# plt.ylim((0,100000))\n",
    "\n",
    "# plt.subplot(1,2,2)\n",
    "# tot = DF[\"cases\"][-1:] + DF[\"recovered\"][-1:] + DF[\"deaths\"][-1:]\n",
    "\n",
    "# plt.axvline(model.stan_data['t0'],color='k', linestyle=\"dashed\")\n",
    "plt.legend()\n",
    "plt.title(roi)\n",
    "plt.figure()\n",
    "# plt.plot(fit.extract()['lp__'])\n",
    "az.plot_density(fit,group='posterior',var_names=[\"theta\",\"R_0\"])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x137064ed0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# theta = az.from_pystan(posterior=fit, coords={'a': ['sigmaC','sigmaR', 'sigmaD', 'q','beta','inittheta']}, dims={'theta': ['a']})\n",
    "\n",
    "\n",
    "# az.plot_pair(theta,var_names=[\"theta\"], coords={'a':['sigmaC','beta']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x136eaacd0>]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lbC = []\n",
    "# ubC = []\n",
    "# lbR = []\n",
    "# ubR = []\n",
    "# lbD = []\n",
    "# ubD = []\n",
    "# for i in range(1,47):\n",
    "# #     print(i)\n",
    "#     x = fit.stansummary(pars=[f'u_predict[{i},1]'], probs=(0.25,0.975), digits_summary=2).split('\\n')[5]\n",
    "# #     print(x.split('\\t'))\n",
    "#     lbC.append(model.stan_data['n_scale']*float(x.split(' ')[5]))\n",
    "#     ubC.append(model.stan_data['n_scale']*float(x.split(' ')[6]))\n",
    "#     x = fit.stansummary(pars=[f'u_predict[{i},2]'], probs=(0.25,0.975), digits_summary=2).split('\\n')[5]\n",
    "# #     print(x.split(' '))\n",
    "#     lbR.append(model.stan_data['n_scale']*float(x.split(' ')[3]))\n",
    "#     ubR.append(model.stan_data['n_scale']*float(x.split(' ')[4]))\n",
    "# #     x = fit.stansummary(pars=[f'u_predict[{i},4]'], probs=(0.25,0.975), digits_summary=2).split('\\n')[5]\n",
    "# #     print(x.split(' '))\n",
    "# #     lbD.append(model.stan_data['n_scale']*float(x.split(' ')[5]))\n",
    "# #     ubD.append(model.stan_data['n_scale']*float(x.split(' ')[6]))\n",
    "\n",
    "# plt.plot(lbC)\n",
    "# plt.plot(ubC)\n",
    "# plt.plot(lbR)\n",
    "# plt.plot(ubR)\n",
    "# plt.plot(lbD)\n",
    "# plt.plot(ubD)\n",
    "\n",
    "# fit.stansummary(pars=['u[1,1]'], probs=(0.25,0.975), digits_summary=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "conflicting sizes for dimension 'a': length 48 on the data but length 1 on coordinate 'a'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-29-29df276bc4ea>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mu\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pystan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mposterior\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoords\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'a'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'11'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'u'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'a'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m axes = az.plot_forest(\n\u001b[1;32m      5\u001b[0m     \u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/arviz/data/io_pystan.py\u001b[0m in \u001b[0;36mfrom_pystan\u001b[0;34m(posterior, posterior_predictive, predictions, prior, prior_predictive, observed_data, constant_data, predictions_constant_data, log_likelihood, coords, dims, posterior_model, prior_model)\u001b[0m\n\u001b[1;32m    763\u001b[0m             \u001b[0mlog_likelihood\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlog_likelihood\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    764\u001b[0m             \u001b[0mcoords\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcoords\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 765\u001b[0;31m             \u001b[0mdims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdims\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    766\u001b[0m         ).to_inference_data()\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/arviz/data/io_pystan.py\u001b[0m in \u001b[0;36mto_inference_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    218\u001b[0m         return InferenceData(\n\u001b[1;32m    219\u001b[0m             **{\n\u001b[0;32m--> 220\u001b[0;31m                 \u001b[0;34m\"posterior\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mposterior_to_xarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    221\u001b[0m                 \u001b[0;34m\"sample_stats\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample_stats_to_xarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    222\u001b[0m                 \u001b[0;34m\"log_likelihood\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog_likelihood_to_xarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/arviz/data/base.py\u001b[0m in \u001b[0;36mwrapped\u001b[0;34m(cls, *args, **kwargs)\u001b[0m\n\u001b[1;32m     35\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprop_i\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mprop_i\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mprop\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 37\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     38\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/arviz/data/io_pystan.py\u001b[0m in \u001b[0;36mposterior_to_xarray\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     72\u001b[0m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_draws\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mposterior\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mignore\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mdict_to_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlibrary\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpystan\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoords\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoords\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdims\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     75\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mrequires\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"posterior\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/arviz/data/base.py\u001b[0m in \u001b[0;36mdict_to_dataset\u001b[0;34m(data, attrs, library, coords, dims)\u001b[0m\n\u001b[1;32m    199\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    200\u001b[0m         data_vars[key] = numpy_to_data_array(\n\u001b[0;32m--> 201\u001b[0;31m             \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvar_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoords\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcoords\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdims\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    202\u001b[0m         )\n\u001b[1;32m    203\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mxr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_vars\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata_vars\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmake_attrs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlibrary\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlibrary\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/arviz/data/base.py\u001b[0m in \u001b[0;36mnumpy_to_data_array\u001b[0;34m(ary, var_name, coords, dims)\u001b[0m\n\u001b[1;32m    164\u001b[0m     \u001b[0;31m# filter coords based on the dims\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m     \u001b[0mcoords\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mxr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIndexVariable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcoords\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdims\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 166\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mxr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataArray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mary\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoords\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcoords\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdims\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    167\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    168\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/xarray/core/dataarray.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, coords, dims, name, attrs, indexes, fastpath)\u001b[0m\n\u001b[1;32m    342\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_data_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoords\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdims\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    343\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mas_compatible_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 344\u001b[0;31m             \u001b[0mcoords\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdims\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_infer_coords_and_dims\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoords\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdims\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    345\u001b[0m             \u001b[0mvariable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mVariable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdims\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfastpath\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    346\u001b[0m             indexes = dict(\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/xarray/core/dataarray.py\u001b[0m in \u001b[0;36m_infer_coords_and_dims\u001b[0;34m(shape, coords, dims)\u001b[0m\n\u001b[1;32m    153\u001b[0m                     \u001b[0;34m\"conflicting sizes for dimension %r: \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    154\u001b[0m                     \u001b[0;34m\"length %s on the data but length %s on \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 155\u001b[0;31m                     \u001b[0;34m\"coordinate %r\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msizes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    156\u001b[0m                 )\n\u001b[1;32m    157\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: conflicting sizes for dimension 'a': length 48 on the data but length 1 on coordinate 'a'"
     ]
    }
   ],
   "source": [
    "# u = az.from_pystan(posterior=fit, coords={'a': ['11']}, dims={'u': ['a']})\n",
    "\n",
    "\n",
    "# axes = az.plot_forest(\n",
    "#     u,\n",
    "#     kind=\"forestplot\",\n",
    "#     var_names= ['u'],\n",
    "#     coords = {'a':['0']},\n",
    "#     combined=True,\n",
    "#     ridgeplot_overlap=1.5,\n",
    "#     colors=\"blue\",\n",
    "#     figsize=(9, 4),\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(np.shape(fit.extract()['u']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  as.data.frame(summary(fit)[['u']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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